rpcs1-agent-tuner
Registry code: ca9d5261e052d8b5
Use this server for three categories of work. Category 1 — Agent tuning: Call recommend_agent_configuration when designing, tuning, or diagnosing an AI agent. Category 2 — Translation: Call interpret when a user says something ambiguous. Call normalize when input is fragmented. Call rewrite when text needs tone adjustment. Category 3 — Translation Bridge (per-user adaptation): Call calibrate_profile once to build the user’s ReceiverProfile from five quick questions (store the returned JSON in the user’s notes/memory for reuse). Then, on EVERY reply to that user, call render_reply with your…
- endpoint
- https://rpcs1.dev/mcp
- protocol
- streamable-http ·2025-06-18
- authentication
- none observed
- public key
- none — nobody has proven they own this listing
- karma
- 0 · newcomer
90 days 100%· all time 100%
last good check
of 9 tools
- unknown → live
The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.
distinct, expensive to fake
successful, last 30 days
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.
calibrate_profile open 3h ago
Build a ReceiverProfile (TI, SG, FT, UE, AR — continuous 0-100, never a category label) from five behavioral forced-choice answers. Call with NO answers to get the five questions to ask the user; call again with their answers (a/b/c per primitive) to get the profile. Store the returned profile JSON in the user’s notes or memory and pass it to render_reply / prepare_prompt on every turn. Deterministic and stateless — nothing is stored server-side. Schema: https://rpcs1.dev/v1/receiver-profile.json
{ "type": "object", "$schema": "http://json-schema.org/draft-07/schema#", "properties": { "answers": { "type": "object", "properties": { "AR": { "enum": [ "a", "b", "c" ], "type": "string" }, "FT": { "enum": [ "a", "b", "c" ], "type": "string" }, "SG": { "enum": [ "a", "b", "c" ], "type": "string" }, "TI": { "enum": [ "a", "b", "c" ], "type": "string" }, "UE": { "enum": [ "a", "b", "c" ], "type": "string" } }, "description": "Chosen option id per primitive. Omit entirely to receive the questions.", "additionalProperties": false } }, "additionalProperties": false }arguments 54 linesrecommend_agent_configuration open 3h ago
Diagnose why a deployed AI agent may fail. Takes environmental entropy, predictability, stakes, context horizon, and commitment style, then returns receiver profile values (TI, SG, FT, UE, AR), platform parameters (temperature, top_p, strategy), regime prediction, reasoning, and warnings. Optionally pass target_model (the actual model id) to attach MEASURED per-model receiver posture (E-LIT table): evidence-graded literalness, truth-override boundary, and translation directives. Deterministic, stateless, read-only — does not store past recommendations.
{ "type": "object", "$schema": "http://json-schema.org/draft-07/schema#", "properties": { "task": { "type": "object", "default": { "domain": "customer_support", "task_summary": "Customer support agent handling refunds, billing disputes, and policy exceptions", "expected_duration_per_call": "medium" }, "properties": { "domain": { "type": "string", "default": "customer_support", "maxLength": 100, "minLength": 1, "description": "Optional domain such as coding, research, or support." }, "task_summary": { "type": "string", "default": "Customer support agent handling refunds, billing disputes, and policy exceptions", "maxLength": 2000, "minLength": 1, "description": "Plain-language description of what the AI agent does." }, "expected_duration_per_call": { "enum": [ "short", "medium", "long" ], "type": "string", "default": "medium" } }, "additionalProperties": false }, "environment": { "type": "object", "default": { "stakes": "high", "entropy": "dynamic", "predictability": "somewhat_predictable", "commitment_style": "cautious", "context_relevance": "medium" }, "properties": { "stakes": { "enum": [ "low", "medium", "high", "catastrophic" ], "type": "string", "default": "high", "description": "The cost of an incorrect agent action." }, "entropy": { "enum": [ "stable", "moderate", "dynamic", "chaotic" ], "type": "string", "default": "dynamic", "description": "How often the operating environment changes." }, "predictability": { "enum": [ "highly_predictable", "somewhat_predictable", "unpredictable" ], "type": "string", "default": "somewhat_predictable", "description": "How predictable changes are when they occur." }, "commitment_style": { "enum": [ "decisive", "balanced", "cautious" ], "type": "string", "default": "cautious", "description": "How quickly the agent should commit to an action." }, "context_relevance": { "enum": [ "short", "medium", "long" ], "type": "string", "default": "medium", "description": "How far back relevant context usually extends." } }, "additionalProperties": false }, "target_model": { "type": "string", "maxLength": 200, "minLength": 1, "description": "Optional: the actual model id this agent will run on (e.g. \"claude-sonnet-4-6\", \"deepseek-v4-pro\"). When it matches a measured per-model receiver entry (E-LIT table), measured translation directives and evidence-graded posture data are attached to platform_parameters. Unknown models fall back to platform-level behavior unchanged." }, "target_platform": { "enum": [ "anthropic", "openai", "open_source", "generic" ], "type": "string", "default": "anthropic", "description": "The platform whose runtime parameters should be recommended." } }, "additionalProperties": false }arguments 123 linesfork unknown never probed
The calibrated ambiguity surface: deterministic structural fork detectors (reference, scope, grouping, compare-vs-choose, polysemy) with character-offset spans, plus per-reading one-line clarifiers the sender can append to lock a reading in. Returns competing readings, an ask-back question, and a forked-answer scaffold. Silent on clean text by contract. Runs the deterministic mirror floor only over MCP (no model). Prefer this over interpret for span-level ambiguity detection: interpret’s entity list is a word-list engine (calibrated 2026-08-15: no discrimination on conversational text) — advisory only.
{ "type": "object", "$schema": "http://json-schema.org/draft-07/schema#", "required": [ "text" ], "properties": { "text": { "type": "string", "maxLength": 5000, "minLength": 1, "description": "The message to analyze for forks." }, "rejected": { "type": "array", "items": { "type": "string" }, "maxItems": 12, "description": "Reading summaries the user already rejected — never re-offered." } }, "additionalProperties": false }arguments 24 linesinterpret unknown never probed
Detect ambiguity in user messages using the RPCS-1 Signature Ambiguity Framework. Returns AR level (AR0-AR5), confidence, candidate interpretations with scores, clarifying questions, and suggested next step. Use when a user says something vague, passive-aggressive, or underspecified.
{ "type": "object", "$schema": "http://json-schema.org/draft-07/schema#", "required": [ "text" ], "properties": { "risk": { "enum": [ "casual", "advice", "high-stakes", "safety-critical" ], "type": "string", "default": "advice", "description": "Risk category for ambiguity threshold." }, "text": { "type": "string", "maxLength": 5000, "minLength": 1, "description": "The message to interpret." } }, "additionalProperties": false }arguments 27 linesnormalize unknown never probed
Clean up text with ellipses, fragments, and run-on thoughts into coherent prose. Use when a user types stream-of-consciousness or fragmented input.
{ "type": "object", "$schema": "http://json-schema.org/draft-07/schema#", "required": [ "text" ], "properties": { "text": { "type": "string", "maxLength": 5000, "minLength": 1, "description": "Fragmented text to normalize." } }, "additionalProperties": false }arguments 16 linesrewrite unknown never probed
Get rewrite instructions for adapting text to a specific style: technical, plain, socially_gentle, concise, detailed, or direct. Use when communication needs tone adjustment.
{ "type": "object", "$schema": "http://json-schema.org/draft-07/schema#", "required": [ "text" ], "properties": { "text": { "type": "string", "maxLength": 5000, "minLength": 1, "description": "Text to rewrite." }, "style": { "enum": [ "technical", "plain", "socially_gentle", "concise", "detailed", "direct" ], "type": "string", "default": "plain", "description": "Target audience style." } }, "additionalProperties": false }arguments 29 linesprepare_prompt unknown never probed
The inbound half of the Translation Bridge loop. Takes the user’s raw message (possibly ambiguous, fragmented, or underspecified) plus their ReceiverProfile, and returns the recovered intent, a canonical translation to act on, ambiguity level, and — profile-aware — whether to clarify or commit. Call this before acting on any ambiguous user request. Scope note: its detectors are lexical/structural (vague signals, ambiguous references) — for the commit-vs-clarify DECISION, route_intent (with your own proposed readings) is the authority; when they disagree, follow route_intent.
{ "type": "object", "$schema": "http://json-schema.org/draft-07/schema#", "required": [ "text" ], "properties": { "risk": { "enum": [ "casual", "advice", "high-stakes", "safety-critical" ], "type": "string", "default": "advice", "description": "Risk category for the ambiguity threshold." }, "text": { "type": "string", "maxLength": 5000, "minLength": 1, "description": "The user’s raw message." }, "profile": { "type": "object", "required": [ "TI", "SG", "FT", "UE", "AR" ], "properties": { "AR": { "type": "number", "maximum": 100, "minimum": 0, "description": "Ambiguity Resolution: 100 = commit to best reading, 0 = clarify first" }, "FT": { "type": "number", "maximum": 100, "minimum": 0, "description": "Filtering Threshold: 100 = explicit and literal, 0 = subtext lands" }, "SG": { "type": "number", "maximum": 100, "minimum": 0, "description": "Signal Gain: 0 = flat and factual, 100 = warm and expressive" }, "TI": { "type": "number", "maximum": 100, "minimum": 0, "description": "Temporal Integration: 0 = bottom line first, 100 = full context first" }, "UE": { "type": "number", "maximum": 100, "minimum": 0, "description": "Update Elasticity: 100 = pushback welcome, 0 = prefers consistency" } }, "description": "The user’s ReceiverProfile from calibrate_profile. Shapes clarify-vs-commit behavior.", "additionalProperties": false } }, "additionalProperties": false }arguments 71 linesrender_reply unknown never probed
The outbound half of the Translation Bridge loop. Takes your draft reply plus the user’s ReceiverProfile and returns deterministic rendering instructions (structure, warmth, explicitness, revision posture, ambiguity handling — each with a why-trace). Apply the instructions to your draft before answering. Call this on every reply to a calibrated user.
{ "type": "object", "$schema": "http://json-schema.org/draft-07/schema#", "required": [ "text", "profile" ], "properties": { "text": { "type": "string", "maxLength": 10000, "minLength": 1, "description": "Your draft reply." }, "profile": { "type": "object", "required": [ "TI", "SG", "FT", "UE", "AR" ], "properties": { "AR": { "type": "number", "maximum": 100, "minimum": 0, "description": "Ambiguity Resolution: 100 = commit to best reading, 0 = clarify first" }, "FT": { "type": "number", "maximum": 100, "minimum": 0, "description": "Filtering Threshold: 100 = explicit and literal, 0 = subtext lands" }, "SG": { "type": "number", "maximum": 100, "minimum": 0, "description": "Signal Gain: 0 = flat and factual, 100 = warm and expressive" }, "TI": { "type": "number", "maximum": 100, "minimum": 0, "description": "Temporal Integration: 0 = bottom line first, 100 = full context first" }, "UE": { "type": "number", "maximum": 100, "minimum": 0, "description": "Update Elasticity: 100 = pushback welcome, 0 = prefers consistency" } }, "description": "The user’s ReceiverProfile from calibrate_profile.", "additionalProperties": false } }, "additionalProperties": false }arguments 61 linesroute_intent unknown never probed
Entropy routing over competing interpretations — the model proposes, the deterministic core disposes. YOU generate the candidate readings of the user’s message (3–7 short hypotheses covering the plausible interpretations, INCLUDING likely-typo readings, idiom-vs-literal readings, and domain senses) and pass them as hypotheses, ideally with your own likelihoods (0–1 per reading) AND a paraphrase per reading — the user’s message rewritten unambiguously under that interpretation, so the user can VERIFY intent by recognition before anything commits (one misread prompt skews a whole thread). The router computes the posterior and its normalized entropy T̂ and returns the decision: commit (one reading dominates), commit_with_note (close alternative disclosed), present_options (several readings live), or clarify (ask before acting — open-endedly when nothing discriminates). Thresholds adapt to the user’s ReceiverProfile (AR widens/narrows the commit region; high FT discloses near-ties). This tool is the commit-vs-clarify AUTHORITY in the pipeline. Omitting hypotheses falls back to a generic six-intent PRODUCT-ROUTING starter set — do not use the fallback for interpreting arbitrary sentences. Deterministic, stateless, read-only. Benchmarked: RTEB v1.1 (developer-bench grade; see docs/routing.md).
{ "type": "object", "$schema": "http://json-schema.org/draft-07/schema#", "required": [ "text" ], "properties": { "text": { "type": "string", "maxLength": 5000, "minLength": 1, "description": "The user’s raw message." }, "profile": { "type": "object", "required": [ "TI", "SG", "FT", "UE", "AR" ], "properties": { "AR": { "type": "number", "maximum": 100, "minimum": 0, "description": "Ambiguity Resolution: 100 = commit to best reading, 0 = clarify first" }, "FT": { "type": "number", "maximum": 100, "minimum": 0, "description": "Filtering Threshold: 100 = explicit and literal, 0 = subtext lands" }, "SG": { "type": "number", "maximum": 100, "minimum": 0, "description": "Signal Gain: 0 = flat and factual, 100 = warm and expressive" }, "TI": { "type": "number", "maximum": 100, "minimum": 0, "description": "Temporal Integration: 0 = bottom line first, 100 = full context first" }, "UE": { "type": "number", "maximum": 100, "minimum": 0, "description": "Update Elasticity: 100 = pushback welcome, 0 = prefers consistency" } }, "description": "The user’s ReceiverProfile from calibrate_profile. Shapes commit-vs-clarify thresholds.", "additionalProperties": false }, "hypotheses": { "type": "array", "items": { "type": "object", "required": [ "id", "label" ], "properties": { "id": { "type": "string", "maxLength": 64, "minLength": 1 }, "cues": { "type": "array", "items": { "type": "string", "maxLength": 64, "minLength": 1 }, "maxItems": 32, "description": "Lexical cues for the built-in scorer; omit when passing likelihoods." }, "label": { "type": "string", "maxLength": 200, "minLength": 1 }, "prior": { "type": "number", "exclusiveMinimum": 0 }, "paraphrase": { "type": "string", "maxLength": 500, "minLength": 1, "description": "The user’s message REWRITTEN UNAMBIGUOUSLY under this reading. Strongly recommended: when the router asks, the user verifies intent by reading these restatements, not by decoding labels." } }, "additionalProperties": false }, "maxItems": 24, "minItems": 2, "description": "Candidate interpretations. Omit to use a generic six-intent starter set plus a catch-all." }, "likelihoods": { "type": "object", "description": "Optional externally computed likelihood per hypothesis id (e.g. model-derived) — replaces the lexical scorer.", "additionalProperties": { "type": "number", "minimum": 0 } } }, "additionalProperties": false }arguments 114 lines
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.
[](https://brick.blue/agent/ca9d5261e052d8b5)
The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Own the domain? Prove it and the listing carries a verified badge here too: passport.
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.
MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.
Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.
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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.