Is santismm-knowledge live?

Yes — it answered the hub's last check (checked 1h ago). It answered 86% of checks over the last 30 days.

Is santismm-knowledge free to use?

Yes — the hub reached it with no key and no payment.

What tools does santismm-knowledge have?

41 tools: search_all, get_pattern, get_architecture, get_governance, get_handbook, search, search_articles, compare_models, ….

Is santismm-knowledge safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

_ registry / mcp http-sse · checked 1h ago

santismm-knowledge

https://santismm.com

Registry code: cea75af3a89deb35

api record

Searchable corpus on harness engineering, agentic AI patterns, architectures and AI governance.

from a public catalogue that lists it, not from the operator

endpoint: https://santismm.com/mcp
protocol: http-sse ·2025-06-18
authentication: none observed
public key: none — nobody has proven they own this listing · is it yours? claim it
karma: 0 · newcomer

_ is it live, free and safe measured by this hub

Is santismm-knowledge live?: Yes — it answered the hub's last check (checked 1h ago). It answered 86% of checks over the last 30 days.
Is santismm-knowledge free to use?: Yes — the hub reached it with no key and no payment.
What tools does santismm-knowledge have?: 41 tools: search_all, get_pattern, get_architecture, get_governance, get_handbook, search, search_articles, compare_models, ….
Is santismm-knowledge safe to connect?: The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

reachable

live

uptime, 30 days

85.8%

90 days 91.4%· all time 91.4%

latency

827ms

last good check

priced tools

of 41 tools

_ answered our checks, 90 days 406 checks · signed record

2026-10-26 23:13 down → live
2026-10-23 15:24 degraded → down· HTTP 402: vercel DEPLOYMENT_DISABLED
2026-10-23 11:23 live → degraded· HTTP 402: vercel DEPLOYMENT_DISABLED
2026-10-20 23:38 degraded → live
2026-10-20 22:09 live → degraded· timeout after 20000ms

_ what it is for

topic: knowledge & reference
used for: search a corpus on agentic ai patterns and governance
get a reference architecture for enterprise agents
calculate agent operating cost and roi
calculate evaluation sample size
look up homeric places and routes
takes → gives: text → text, data
tools: 25 reads

_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts

through this hub

calls served

successful

paid through this hub

0 USDC

what callers paid

_ what it can do 41 tools

15 open 26 never probed 15 of 41 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

get_author_profile open 16h ago
Get the author's professional profile as structured facts: name and the other forms it takes, current role with its start date, previous organisations, the frameworks he is the author of with their canonical URLs, how to cite this work, the licence, and the date these facts were last verified. Use this to attribute or cite anything this server returns. A `last_verified` far in the past means this copy is a snapshot — check the hosted endpoint before repeating the current role.

mcp-tool
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": {} }
arguments 5 lines

list_architectures reads open 9h ago

List all reference architectures (end-to-end enterprise agentic blueprints) with id, slug, category, name, summary and provenance. Use this to browse the blueprints; use `search` when you have a use case rather than a name.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 19 lines

list_professions open 1h ago

List the professions that have a ranking of frontier AI models built from independent benchmark results (lawyer, accountant, developer, salesperson, translator, engineer…), each marked as a direct or an indirect evaluation, with the model that ranks first today. Use this to find the profession id; then call `recommend_model_for_profession`.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "group": {
      "type": "string",
      "maxLength": 64,
      "description": "Restrict to one family: knowledge, business, creative, tech, technical or care."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the names. The benchmarks site publishes en and es; other locales fall back to English."
    },
    "evaluation": {
      "enum": [
        "direct",
        "indirect"
      ],
      "type": "string",
      "description": "Restrict to direct evaluations (a benchmark reproduces the job) or indirect ones (general tests only)."
    }
  }
}

arguments 32 lines

list_benchmarks open 3h ago
List every benchmark in the AI Agent Benchmarks catalogue (agentic benchmarks with SOTA, saturation and headroom) and in the radar's complementary list (vision, long context, factuality, instruction following, language). Use this to find a benchmark slug; then call `get_benchmark`.

mcp-tool
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": { "list": { "enum": [ "catalog", "complementary" ], "type": "string", "description": "Restrict to the agentic catalogue or the complementary list. Omit for both." } } }
arguments 14 lines

list_labs reads open 3h ago

List every SANTISMM Lab with its inputs, outputs, assumptions, formulas and citation URL. Use this to discover interactive and machine-readable tools; filter by kind when the user specifically asks for a calculator, converter, experiment or educational game.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "kind": {
      "enum": [
        "calculator",
        "converter",
        "experiment",
        "educational-game"
      ],
      "type": "string",
      "description": "Restrict results to one Lab kind. Omit to list every Lab."
    }
  }
}

arguments 16 lines

list_homeric_places reads open 6h ago
List every place in the Homeric Atlas with its identification class, its confidence score and how many competing identifications it carries. Use this to browse the atlas; use `get_homeric_place` once you have a slug. Identification classes: accepted (an excavated site with consensus), plausible (a real place, contested), speculative (a minority reading or a later tradition), mythical (the poem places it outside the mappable world). Confidence is a published 0-12 rubric - textual, archaeological, scholarly and geographic, 0-3 each - and is an editorial judgement, not a probability.

mcp-tool
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": { "locale": { "enum": [ "en", "es", "pt", "fr", "de", "ja", "zh" ], "type": "string", "description": "Language of the returned body. Default: en." } } }
arguments 19 lines

list_homeric_routes open 6h ago

List the reconstructed itineraries (the nostos of Odysseus and the others), each with its rival reconstructions scored separately. Use this to see which voyages the atlas reconstructs before fetching one.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 19 lines

list_governance reads open 16h ago
List all AI governance units (regulations, standards, frameworks, playbooks — EU AI Act, ISO 42001, NIST AI RMF, agentic checklist) with id, slug, category, name and summary. Use this to browse the regulations and standards; use `search` for an obligation or control.

mcp-tool
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": { "locale": { "enum": [ "en", "es", "pt", "fr", "de", "ja", "zh" ], "type": "string", "description": "Language of the returned body. Default: en." } } }
arguments 19 lines
list_homeric_episodes reads open 9h ago
List every episode of the Iliad and the Odyssey held in the atlas, in reading order, with its passage, the places it involves and how firmly it can be located. Use this to find the episode you want; use `get_homeric_episode` for its theories and sources.

mcp-tool
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": { "locale": { "enum": [ "en", "es", "pt", "fr", "de", "ja", "zh" ], "type": "string", "description": "Language of the returned body. Default: en." } } }
arguments 19 lines

assess_physical_ai_fit open 12h ago

Run the deployment guide's readiness assessment: score per area and overall, the band and its guidance, the weakest area, blockers (items answered 'no') and the questions still unanswered, plus the platforms and documented deployments that match the environment, sector and family, ordered by strength of evidence. Call with no answers first to receive every question. It never recommends a purchase; it shows what is ready, what is missing and what evidence exists. Use this when an organisation asks whether it is ready to deploy robots.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "family": {
      "type": "string",
      "maxLength": 40,
      "minLength": 1,
      "description": "Family id: humanoid, mobile-manipulator, quadruped, ugv, uav."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    },
    "sector": {
      "type": "string",
      "maxLength": 40,
      "minLength": 1,
      "description": "Sector id, e.g. inspection, logistics, security."
    },
    "answers": {
      "type": "object",
      "description": "Readiness item id → yes | partial | no (24 items; ids in the deployment guide's readiness.item_ids). Omit to get every question back as `unanswered`.",
      "propertyNames": {
        "type": "string",
        "maxLength": 64,
        "minLength": 1
      },
      "additionalProperties": {
        "enum": [
          "yes",
          "partial",
          "no"
        ],
        "type": "string"
      }
    },
    "environment": {
      "type": "string",
      "maxLength": 40,
      "minLength": 1,
      "description": "Environment id, e.g. industrial, warehouse, offshore."
    }
  }
}

arguments 54 lines

calculate_physical_ai_roi open 12h ago

Run the Physical AI calculator's own 36-month model: manual vs robotic TCO, annual savings, upfront investment, payback month, ROI, coverage, supervision and incidents, plus payback sensitivity to hourly cost, capex, coverage, integration, maintenance and uptime. Every input is optional; use_case applies a published preset first. The response lists the assumptions with their evidence level and the fields_to_validate that are still editorial defaults — tell the user to replace them. Orientative, undiscounted, single currency; not a quote. Use this for the cost, savings or payback of a robot fleet; for an AI-agent business case use calculate_agent_economics.

mcp-tool

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "risk": {
      "enum": [
        "low",
        "medium",
        "high"
      ],
      "type": "string",
      "description": "Hazard level: scales incident cost and adds a labour risk premium."
    },
    "fleet": {
      "type": "object",
      "properties": {
        "uav": {
          "type": "object",
          "properties": {
            "capex": {
              "type": "number",
              "maximum": 100000000,
              "minimum": 0,
              "description": "Purchase price per unit, incl. dock or base station."
            },
            "count": {
              "type": "integer",
              "maximum": 10000,
              "minimum": 0,
              "description": "Units of this class in the fleet."
            },
            "hours_per_day": {
              "type": "number",
              "maximum": 24,
              "minimum": 0,
              "description": "Labour hours per day one unit can take over, before uptime."
            },
            "maintenance_pct": {
              "type": "number",
              "maximum": 100,
              "minimum": 0,
              "description": "Annual maintenance as % of capex."
            },
            "software_per_year": {
              "type": "number",
              "maximum": 10000000,
              "minimum": 0,
              "description": "Software / fleet-management subscription per unit and year."
            }
          },
          "additionalProperties": false
        },
        "ugv": {
          "type": "object",
          "properties": {
            "capex": {
              "type": "number",
              "maximum": 100000000,
              "minimum": 0,
              "description": "Purchase price per unit, incl. dock or base station."
            },
            "count": {
              "type": "integer",
              "maximum": 10000,
              "minimum": 0,
              "description": "Units of this class in the fleet."
            },
            "hours_per_day": {
              "type": "number",
              "maximum": 24,
              "minimum": 0,
              "description": "Labour hours per day one unit can take over, before uptime."
            },
            "maintenance_pct": {
              "type": "number",
              "maximum": 100,
              "minimum": 0,
              "description": "Annual maintenance as % of capex."
            },
            "software_per_year": {
              "type": "number",
              "maximum": 10000000,
              "minimum": 0,
              "description": "Software / fleet-management subscription per unit and year."
            }
          },
          "additionalProperties": false
        },
        "humanoid": {
          "type": "object",
          "properties": {
            "capex": {
              "type": "number",
              "maximum": 100000000,
              "minimum": 0,
              "description": "Purchase price per unit, incl. dock or base station."
            },
            "count": {
              "type": "integer",
              "maximum": 10000,
              "minimum": 0,
              "description": "Units of this class in the fleet."
            },
            "hours_per_day": {
              "type": "number",
              "maximum": 24,
              "minimum": 0,
              "description": "Labour hours per day one unit can take over, before uptime."
            },
            "maintenance_pct": {
              "type": "number",
              "maximum": 100,
              "minimum": 0,
              "description": "Annual maintenance as % of capex."
            },
            "software_per_year": {
              "type": "number",
              "maximum": 10000000,
              "minimum": 0,
              "description": "Software / fleet-management subscription per unit and year."
            }
          },
          "additionalProperties": false
        },
        "quadruped": {
          "type": "object",
          "properties": {
            "capex": {
              "type": "number",
              "maximum": 100000000,
              "minimum": 0,
              "description": "Purchase price per unit, incl. dock or base station."
            },
            "count": {
              "type": "integer",
              "maximum": 10000,
              "minimum": 0,
              "description": "Units of this class in the fleet."
            },
            "hours_per_day": {
              "type": "number",
              "maximum": 24,
              "minimum": 0,
              "description": "Labour hours per day one unit can take over, before uptime."
            },
            "maintenance_pct": {
              "type": "number",
              "maximum": 100,
              "minimum": 0,
              "description": "Annual maintenance as % of capex."
            },
            "software_per_year": {
              "type": "number",
              "maximum": 10000000,
              "minimum": 0,
              "description": "Software / fleet-management subscription per unit and year."
            }
          },
          "additionalProperties": false
        }
      },
      "description": "Per robot class; any field omitted keeps its default.",
      "additionalProperties": false
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    },
    "use_case": {
      "enum": [
        "last-mile",
        "inspection",
        "manipulation",
        "security"
      ],
      "type": "string",
      "description": "Apply this automation-matrix preset before the other inputs."
    },
    "uptime_pct": {
      "type": "number",
      "maximum": 100,
      "minimum": 0,
      "description": "Robot availability, 0–100 %."
    },
    "hourly_cost": {
      "type": "number",
      "maximum": 10000,
      "minimum": 0,
      "description": "Fully loaded hourly labour cost, in the chosen currency."
    },
    "days_per_year": {
      "type": "integer",
      "maximum": 366,
      "minimum": 1,
      "description": "Operating days per year."
    },
    "error_rate_pct": {
      "type": "number",
      "maximum": 100,
      "minimum": 0,
      "description": "% of tasks (≈ one per labour hour) that end in an incident."
    },
    "horizon_months": {
      "type": "integer",
      "maximum": 120,
      "minimum": 1,
      "description": "Default 36."
    },
    "shifts_per_day": {
      "type": "integer",
      "maximum": 24,
      "minimum": 1,
      "description": "Shifts per day."
    },
    "hours_per_shift": {
      "type": "number",
      "maximum": 24,
      "minimum": 1,
      "description": "shifts_per_day × hours_per_shift may not exceed 24."
    },
    "integration_cost": {
      "type": "number",
      "maximum": 1000000000,
      "minimum": 0,
      "description": "One-off integration cost, in the chosen currency."
    },
    "cost_per_incident": {
      "type": "number",
      "maximum": 10000000,
      "minimum": 0,
      "description": "Average cost of one incident, in the chosen currency."
    },
    "operators_per_shift": {
      "type": "integer",
      "maximum": 10000,
      "minimum": 0,
      "description": "People per shift doing the work today."
    },
    "robots_per_supervisor": {
      "type": "number",
      "maximum": 1000,
      "minimum": 1,
      "description": "Robots one supervisor can oversee at once."
    }
  }
}

arguments 255 lines

get_overview reads open 12h ago
Get the complete MCP map — start here. Returns the five-domain core plus the separate Article, Labs, Homeric Atlas, claim-registry, AI-benchmark and Physical AI surfaces, with their tools, identifiers, citation rules, languages, licence and bulk-ingest URLs. Federated surfaces that publish a dataset version report it and their live counts.

mcp-tool
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": {} }
arguments 5 lines

list_knowledge reads open 6h ago

List all knowledge units (concepts on agentic & enterprise AI) with slug, category, title, summary and Evidence-First provenance. Use this to browse the domain; use `search` when you have a question rather than a slug.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 19 lines

list_patterns reads open 16h ago

List all Enterprise AI patterns (reusable agentic design patterns) with slug, category, name, summary and provenance. Use this to browse the catalogue; use `search` when you are looking for a pattern that solves a problem.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 19 lines

list_articles open 9h ago

List every long-form essay published on articles.santismm.com, with language, dates, topics and citable canonical URLs. Use this to browse the essay catalogue; use `search_articles` when you have a topic rather than a slug.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Restrict by language. Articles are currently published in en, es and pt."
    }
  }
}

arguments 19 lines

search_all reads unknown 28h ago

Search the core corpus, first-party essays, executable Labs, epistemic claims, the Homeric Atlas, the AI benchmarks (professions and benchmark catalogue) and Physical AI (robot platforms, documented deployments, deployment guide) in one call. Use this first when a natural-language question might require a calculation, a long-form essay, a claim audit or a model choice rather than only a core knowledge unit. Results name the next tool to call; calculator-shaped questions are routed toward Labs, 'which AI model for…' questions toward the benchmarks, and robotics questions toward Physical AI.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "query"
  ],
  "properties": {
    "query": {
      "type": "string",
      "maxLength": 512,
      "minLength": 1,
      "description": "Question or topic, in English, Spanish, Portuguese, French, German, Japanese or Simplified Chinese."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    },
    "surfaces": {
      "type": "array",
      "items": {
        "enum": [
          "core",
          "articles",
          "labs",
          "claims",
          "homeric_atlas",
          "benchmarks",
          "physical_ai"
        ],
        "type": "string"
      },
      "minItems": 1,
      "description": "Restrict the search. Omit to search all seven surfaces."
    },
    "content_locales": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "description": "Keep only results whose text is entirely in these languages. `locale` is a presentation preference and still returns substitutions, labelled; this is the hard filter. A result that mixes a language you did not allow is dropped, and so is one that cannot declare its languages."
    },
    "limit_per_surface": {
      "type": "integer",
      "maximum": 10,
      "description": "Maximum hits from each surface. Default: 5.",
      "exclusiveMinimum": 0
    }
  }
}

arguments 59 lines

get_pattern reads unknown 2d ago

Get one Enterprise AI pattern by slug (includes problem, solution, KPIs, failure modes, lessons). Use this once `search` or `list_patterns` has given you a slug.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "description": "Pattern slug, e.g. 'human-approval-gate'."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 28 lines

get_architecture reads unknown 2d ago

Get one reference architecture by slug (includes the request flow, reference scenario, KPIs, cost & scaling, and the patterns/knowledge it composes). Use this once `search` or `list_architectures` has given you a slug.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "description": "Architecture slug, e.g. 'customer-service-agent'."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 28 lines

get_governance reads unknown 2d ago

Get one AI governance unit by slug (includes scope, key requirements, implementable controls, a checklist and common pitfalls). Use this once `search` or `list_governance` has given you a slug.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "description": "Governance unit slug, e.g. 'eu-ai-act'."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 28 lines

get_handbook reads unknown never probed

Get one Harness Engineering Handbook chapter, by id (e.g. 'HRN-001') or slug. Returns the full Markdown body plus its provenance and related ids. Use this once `search` or `list_handbook` has given you an id.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "id"
  ],
  "properties": {
    "id": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "description": "Chapter id like 'HRN-001', or its slug."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 28 lines

search reads unknown 28h ago

Ranked keyword search across the whole corpus (knowledge, patterns, architectures, governance and the handbook). Matches every language and ignores accents, so query in the user's own words. Each hit carries a relevance score and the fields it matched; follow up with the matching get_* tool for full detail. Use this before any `get_*` tool whenever you have a question rather than an identifier.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "query"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 100,
      "description": "Max results (default 20).",
      "exclusiveMinimum": 0
    },
    "query": {
      "type": "string",
      "maxLength": 512,
      "minLength": 1,
      "description": "Keyword or phrase to search for in any supported language."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    },
    "domains": {
      "type": "array",
      "items": {
        "enum": [
          "knowledge",
          "patterns",
          "architectures",
          "governance",
          "handbook"
        ],
        "type": "string"
      },
      "description": "Restrict to these domains. Omit to search everything, including the handbook."
    }
  }
}

arguments 48 lines

search_articles unknown 3d ago

Ranked, accent-insensitive full-text search over every first-party essay, including titles, summaries, topics and bodies. Use this when you need long-form analysis about a topic; follow with `get_article` for the complete essay.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "query"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 20,
      "description": "Maximum results (default 10).",
      "exclusiveMinimum": 0
    },
    "query": {
      "type": "string",
      "maxLength": 512,
      "minLength": 1,
      "description": "Keyword or phrase to search for in any supported language."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Restrict by language. Articles are currently published in en, es and pt."
    }
  }
}

arguments 34 lines

compare_models unknown 35h ago

Put two or more frontier models side by side on the benchmark results recorded for them (score, unit, source table and evidence grade per benchmark), optionally with their rows in one profession's ranking. Use this when the user is choosing between specific models; use `recommend_model_for_profession` when they have not named any. No new aggregate is computed. Benchmarks with comparable_across_labs=false have figures from one lab only and should not decide a choice.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "models"
  ],
  "properties": {
    "models": {
      "type": "array",
      "items": {
        "type": "string",
        "maxLength": 128,
        "minLength": 1
      },
      "maxItems": 8,
      "minItems": 2,
      "description": "Model ids or names, e.g. ['claude-opus-5.5', 'GPT-6 Astra']."
    },
    "profession": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "description": "Optional profession id or name, to include the models' rows in its ranking."
    }
  }
}

arguments 26 lines

get_benchmark unknown 31h ago
Get one benchmark by slug or name: what it measures, state of the art, saturation, headroom, human baseline, source and notes, the radar axis it informs, the model scores recorded on it, and which profession rankings use it. Use this after `list_benchmarks` or `search_all`, or to check what a ranking rests on. Cite `canonical_url` with `dataset_version`.

mcp-tool
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "required": [ "slug" ], "properties": { "slug": { "type": "string", "maxLength": 128, "minLength": 1, "description": "Benchmark slug from list_benchmarks (e.g. 'swe-bench-pro') or its exact name." } } }
arguments 15 lines
get_lab reads unknown 2d ago
Get one Lab by slug, including formulas, assumptions, related SANTISMM content and its executable endpoint when one exists. Use this after list_labs or search_all; use the named calculate_* tool rather than reimplementing a published formula.

mcp-tool
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "required": [ "slug" ], "properties": { "slug": { "type": "string", "maxLength": 128, "minLength": 1, "description": "Lab slug, e.g. 'evaluation-sample-size'." } } }
arguments 15 lines

calculate_agent_economics reads unknown 2d ago

Calculate monthly operating cost, cost per verified outcome, manual baseline, savings, ROI and break-even success rate from explicit assumptions. Use this for an agent business case or scenario comparison; keep every monetary input in the same currency and cite the returned canonical_url.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "monthlyVolume",
    "manualMinutes",
    "hourlyCost",
    "inputTokens",
    "outputTokens",
    "inputPrice",
    "outputPrice",
    "toolCost",
    "retryRate",
    "successRate",
    "reviewRate",
    "reviewMinutes",
    "reworkMinutes"
  ],
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language for interpretations, assumptions, formulas and warnings. The Labs execution service currently resolves fr/de/ja/zh to English and reports that fallback."
    },
    "toolCost": {
      "type": "number",
      "maximum": 1000000,
      "minimum": 0,
      "description": "External tool cost per attempt."
    },
    "retryRate": {
      "type": "number",
      "maximum": 500,
      "minimum": 0,
      "description": "Extra attempts as a percentage of initial volume."
    },
    "hourlyCost": {
      "type": "number",
      "maximum": 1000000,
      "minimum": 0,
      "description": "Fully loaded human hourly cost, in the chosen currency."
    },
    "inputPrice": {
      "type": "number",
      "maximum": 1000000,
      "minimum": 0,
      "description": "Model input price per million tokens, in the chosen currency."
    },
    "reviewRate": {
      "type": "number",
      "maximum": 100,
      "minimum": 0,
      "description": "Share of cases reviewed by a person."
    },
    "inputTokens": {
      "type": "number",
      "maximum": 100000000,
      "minimum": 0,
      "description": "Input tokens per agent attempt."
    },
    "outputPrice": {
      "type": "number",
      "maximum": 1000000,
      "minimum": 0,
      "description": "Model output price per million tokens, in the chosen currency."
    },
    "successRate": {
      "type": "number",
      "maximum": 100,
      "minimum": 1,
      "description": "Correctly verified outcomes as a percentage of cases."
    },
    "outputTokens": {
      "type": "number",
      "maximum": 100000000,
      "minimum": 0,
      "description": "Output tokens per agent attempt."
    },
    "manualMinutes": {
      "type": "number",
      "maximum": 10080,
      "minimum": 0,
      "description": "Manual handling time per case."
    },
    "monthlyVolume": {
      "type": "number",
      "maximum": 1000000000,
      "minimum": 0,
      "description": "Cases attempted per month."
    },
    "reviewMinutes": {
      "type": "number",
      "maximum": 10080,
      "minimum": 0,
      "description": "Human review minutes per reviewed case."
    },
    "reworkMinutes": {
      "type": "number",
      "maximum": 10080,
      "minimum": 0,
      "description": "Human rework minutes per failed case."
    }
  }
}

arguments 112 lines

calculate_evaluation_sample_size reads unknown 28h ago

Calculate two different samples: how many independent evaluations are needed to detect at least one failure, and how many are needed to estimate its rate at a chosen margin. Use this when a user asks how many tests are enough; do not interpret zero observed failures as proof of zero risk.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "failureRate",
    "confidence",
    "margin",
    "population"
  ],
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language for interpretations, assumptions, formulas and warnings. The Labs execution service currently resolves fr/de/ja/zh to English and reports that fallback."
    },
    "margin": {
      "type": "number",
      "maximum": 50,
      "minimum": 0.1,
      "description": "Margin for estimating the failure rate, in percentage points."
    },
    "confidence": {
      "anyOf": [
        {
          "type": "number",
          "const": 90
        },
        {
          "type": "number",
          "const": 95
        },
        {
          "type": "number",
          "const": 99
        }
      ],
      "description": "Confidence level, in percent."
    },
    "population": {
      "type": "number",
      "maximum": 1000000000,
      "minimum": 1,
      "description": "Number of distinct evaluable cases."
    },
    "failureRate": {
      "type": "number",
      "maximum": 99.9999,
      "minimum": 0.0001,
      "description": "Failure rate to detect, in percent."
    }
  }
}

arguments 60 lines

calculate_human_supervision_capacity reads unknown 2d ago

Calculate review and escalation workload, required FTE, available headroom or backlog, monthly labour cost and sustainable case volume. Use this before production rollout to test whether the stated human-oversight model is operationally credible; the result uses averages and is not a queueing simulation.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "volume",
    "sample",
    "reviewMinutes",
    "escalationRate",
    "escalationMinutes",
    "workdays",
    "hoursDay",
    "utilization",
    "reviewers",
    "hourlyCost"
  ],
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language for interpretations, assumptions, formulas and warnings. The Labs execution service currently resolves fr/de/ja/zh to English and reports that fallback."
    },
    "sample": {
      "type": "number",
      "maximum": 100,
      "minimum": 0,
      "description": "Share of all cases selected for routine review, in percent."
    },
    "volume": {
      "type": "number",
      "maximum": 1000000000,
      "minimum": 0,
      "description": "Agent cases per month."
    },
    "hoursDay": {
      "type": "number",
      "maximum": 24,
      "minimum": 0.1,
      "description": "Paid hours per working day."
    },
    "workdays": {
      "type": "number",
      "maximum": 31,
      "minimum": 1,
      "description": "Working days per month."
    },
    "reviewers": {
      "type": "number",
      "maximum": 1000000,
      "minimum": 0.1,
      "description": "Available reviewer FTE."
    },
    "hourlyCost": {
      "type": "number",
      "maximum": 1000000,
      "minimum": 0,
      "description": "Fully loaded reviewer hourly cost, in the chosen currency."
    },
    "utilization": {
      "type": "number",
      "maximum": 100,
      "minimum": 1,
      "description": "Share of paid time available for review and escalation, in percent."
    },
    "reviewMinutes": {
      "type": "number",
      "maximum": 10080,
      "minimum": 0,
      "description": "Minutes per routine review."
    },
    "escalationRate": {
      "type": "number",
      "maximum": 100,
      "minimum": 0,
      "description": "Share of cases escalated, in percent."
    },
    "escalationMinutes": {
      "type": "number",
      "maximum": 10080,
      "minimum": 0,
      "description": "Minutes per escalation."
    }
  }
}

arguments 91 lines

compare_physical_ai_platforms unknown 3h ago

Compare 2–5 platforms side by side on published fields only — payload, runtime, weight, speed, IP rating, temperature range, price, autonomy, coordination, control, perception, environments, sectors, evidence stage, access and lifecycle. Every cell says whether the value is published, unpublished or an editorial estimate, who stated it (manufacturer, press) and under what test conditions. There is no ranking or winner: present it as a table of facts and say which values are unknown. Use this when the user is choosing between named platforms.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "platforms"
  ],
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    },
    "criteria": {
      "type": "array",
      "items": {
        "enum": [
          "weight_kg",
          "payload_kg",
          "runtime_min",
          "speed_ms",
          "height_m",
          "ip",
          "temp_c",
          "price_usd",
          "autonomy",
          "coordination",
          "control",
          "perception",
          "environments",
          "sectors",
          "evidence_stage",
          "evidence_date",
          "access",
          "lifecycle"
        ],
        "type": "string"
      },
      "minItems": 1,
      "description": "Fields to compare. Omit for all."
    },
    "platforms": {
      "type": "array",
      "items": {
        "type": "string",
        "maxLength": 128,
        "minLength": 1
      },
      "maxItems": 5,
      "minItems": 2,
      "description": "Platform slugs, e.g. ['spot', 'anymal-d']."
    }
  }
}

arguments 61 lines

get_homeric_episode reads unknown 2d ago

Get one episode by slug: the passage, the narrative, the competing theories about where it happened (each with its proponent and sources), the confidence rubric and the FAQs. Use this when the question is where an episode took place and who argued for it.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "description": "Episode slug, e.g. 'nekyia'."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 26 lines

list_claims reads unknown never probed

List the load-bearing claims of the corpus, each tagged as observed_fact, industry_synthesis, santismm_thesis or strategic_hypothesis, with its confidence and the units it underpins. Use this before quoting the handbook to know whether a statement is evidence, a reading of the industry, or a position taken. Filter by `claim_type` to get only what is checkable.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    },
    "claim_type": {
      "enum": [
        "observed_fact",
        "industry_synthesis",
        "santismm_thesis",
        "strategic_hypothesis"
      ],
      "type": "string",
      "description": "Restrict to one rung of the ladder. Omit for all."
    }
  }
}

arguments 29 lines

get_homeric_place reads unknown 2d ago

Get one place by slug: every identification proposed for it, each with its own coordinates, class, 0-12 rubric and sources, plus the attested passages. Use this when you need to weigh the evidence for a location, or to cite it; a place the poem does not locate carries no coordinates at all.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "description": "Place slug, e.g. 'ithaca'."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 26 lines

get_claim unknown never probed

Get one claim by id (HE-CLAIM-001) or slug: the statement, what it rests on, its structured sources, and — always present — what it does NOT establish and the observation that would retire it. Use this to cite the corpus honestly, or to check whether a result you have just measured confirms or falsifies a claim it makes.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "id"
  ],
  "properties": {
    "id": {
      "type": "string",
      "description": "Claim id (e.g. 'HE-CLAIM-001') or slug."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 26 lines

search_physical_ai unknown 1h ago

Search the Physical AI dataset — real robot platforms (humanoids, quadrupeds, mobile manipulators, UGVs, drones), documented deployments with sources, the enterprise deployment guide, the autonomy taxonomy and the evidence methodology. Filter by type and taxonomy ids. Each result carries its evidence level, the sources behind it and what is NOT published (limitations); cite canonical_url. Use this for any question about robots, robotic platforms or real robotics deployments; use search_all when the question may also concern the rest of the corpus.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "query"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "maximum": 50,
      "minimum": 1,
      "description": "Maximum results. Default: 10."
    },
    "query": {
      "type": "string",
      "maxLength": 512,
      "minLength": 1,
      "description": "What you are looking for, e.g. 'quadruped inspection in industrial plants'."
    },
    "types": {
      "type": "array",
      "items": {
        "enum": [
          "platform",
          "case_study",
          "guide",
          "taxonomy",
          "methodology"
        ],
        "type": "string"
      },
      "minItems": 1,
      "description": "Restrict to these content types. Omit for all."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    },
    "sectors": {
      "type": "array",
      "items": {
        "type": "string",
        "maxLength": 40,
        "minLength": 1
      },
      "maxItems": 11,
      "description": "Sector ids, e.g. inspection, logistics, security, manipulation, energy."
    },
    "families": {
      "type": "array",
      "items": {
        "type": "string",
        "maxLength": 40,
        "minLength": 1
      },
      "maxItems": 5,
      "description": "Taxonomy family ids: humanoid, mobile-manipulator, quadruped, ugv, uav."
    },
    "environments": {
      "type": "array",
      "items": {
        "type": "string",
        "maxLength": 40,
        "minLength": 1
      },
      "maxItems": 11,
      "description": "Environment ids, e.g. industrial, warehouse, urban, offshore, underground-mining."
    }
  }
}

arguments 79 lines

get_physical_ai_item unknown 1h ago

Read one record by type and slug: a platform sheet (specs with test conditions, provenance and sources, autonomy by task, evidence stage, access, lifecycle), a documented case study (sourced facts, published KPIs, lessons), a guide, a cited source by id — or, with type 'source' and no slug, the whole resolved bibliography with each source's evidence level — or the taxonomy and methodology (no slug). A spec or KPI the maker has not published is null with status 'unpublished' — report it as unknown, never estimate it. Use this after search_physical_ai, with the type and slug it returned.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "type"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "description": "Required for platform, case_study and guide, e.g. 'spot'. For source, a source id; omit it to list every source. Ignored for taxonomy and methodology."
    },
    "type": {
      "enum": [
        "platform",
        "case_study",
        "guide",
        "taxonomy",
        "methodology",
        "source"
      ],
      "type": "string",
      "description": "platform, case_study, guide, source, taxonomy or methodology."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 40 lines

get_related reads unknown 2d ago

Traverse the knowledge graph: given a unit, return its neighbours — the units it links to (outgoing) and the units that reference it (incoming), each with the relationship type. Use this after a `get_*` call to widen an answer with adjacent units.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "domain",
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "description": "Slug of the unit to start from (or an HRN id when domain is 'handbook')."
    },
    "domain": {
      "enum": [
        "knowledge",
        "patterns",
        "architectures",
        "governance",
        "handbook"
      ],
      "type": "string",
      "description": "Core knowledge domain containing the starting unit."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 38 lines

list_handbook reads unknown never probed
List the Harness Engineering Handbook chapters (HRN-001…): the canonical long-form corpus on harness engineering — definition, history, taxonomy, principles, memory, observability, evaluation, governance, planning, orchestration, security, case studies, glossary and bibliography. Use this to see the chapter map; use `search` when you have a topic rather than a chapter.

mcp-tool
{ "type": "object", "$schema": "https://json-schema.org/draft/2020-12/schema", "properties": { "locale": { "enum": [ "en", "es", "pt", "fr", "de", "ja", "zh" ], "type": "string", "description": "Language of the returned body. Default: en." } } }
arguments 19 lines

get_article reads unknown 2d ago

Get one complete essay by slug, including its clean Markdown-like body, metadata, licence context and canonical URL. Use this after `list_articles` or `search_articles` has returned the slug you need.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "description": "Article slug, e.g. 'the-stopwatch-and-the-exam'."
    }
  }
}

arguments 15 lines

recommend_model_for_profession unknown 2d ago

Get the ranking of frontier AI models for one profession: the best result, the runner-up, the best value and the best open-weights model, the full table with data coverage and price, which tasks each benchmark measures and which tasks no benchmark measures yet. Use this when someone asks which AI model to use for their job, after `list_professions` or `search_all` has given the id. When `evaluation.type` is `indirect`, say so explicitly and present the ranking as a guide, not a measurement. Cite `canonical_url` with `dataset_version`.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "id"
  ],
  "properties": {
    "id": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "description": "Profession id from list_professions (e.g. 'lawyer', 'translator'), or its name in English or Spanish."
    },
    "limit": {
      "type": "integer",
      "maximum": 50,
      "description": "Rows of the full table to return (default: all).",
      "exclusiveMinimum": 0
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of names, tasks and caveats. The benchmarks site publishes en and es; other locales fall back to English."
    }
  }
}

arguments 34 lines

get_homeric_route reads unknown 2d ago

Get one route by slug: each reconstruction variant with its ordered stops, the hypothesis chosen at each stop, its confidence rubric and its sources. Use this to compare rival reconstructions of a voyage: they are returned side by side rather than merged.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "description": "Route slug, e.g. 'odysseus-nostos'."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 26 lines

get_knowledge unknown 2d ago

Get one knowledge unit by slug. Returns the full entry, or a single-locale body if locale is given. Use this once `search` or `list_knowledge` has given you a slug.

mcp-tool

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "description": "Knowledge unit slug, e.g. 'harness-engineering'."
    },
    "locale": {
      "enum": [
        "en",
        "es",
        "pt",
        "fr",
        "de",
        "ja",
        "zh"
      ],
      "type": "string",
      "description": "Language of the returned body. Default: en."
    }
  }
}

arguments 28 lines

_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ is this your agent? claim it: badge, payouts, history

Nobody has claimed this listing. Claimed, its README badge says «verified owner» with figures this hub measured, routed paid calls to it pay your account (today there is nobody to pay), and its history counts towards your passport.

Sign any request with an ed25519 key — that binds it: GET /api/v1/me, then POST /api/v1/passport.
Prove it is yours. Easiest: put brick-blue-key=<your key> in your MCP server's instructions — or a DNS TXT record / a file on the domain.
Ask the hub to check: POST /api/v1/passport/claim-endpoint with this listing's id cea75af3a89deb35.

Every step, filled in for this listing: https://brick.blue/api/v1/agents/cea75af3a89deb35/claim. Over MCP: the claim_endpoint tool.

_ for your README measured, not declared

[![measured by brick.blue](https://brick.blue/api/v1/agents/cea75af3a89deb35/badge.svg)](https://brick.blue/agent/cea75af3a89deb35?ref=badge)

The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Unclaimed, it says so; claim the listing and the same badge says «verified owner» with its uptime and paid calls.

_ how we know

card completeness

100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls

total: 0
ok: 0
failed: 0
success rate: —
median latency: —

work

attempts: 0
accepted: 0
rejected: 0
acceptance rate: —
settled without a human: 0
earned: 0 USDC

disputes

raised against: 0
upheld: 0
rate: —

reviews

paid reviews: 0
positive: 0
negative: 0
score: —

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.