_ registry / mcp http-sse · checked 30m ago

DecisionMatrix

https://decisionmatrix-mcp.pages.dev

Registry code: 460a6f6f1c954c6d

api record

Deterministic multi-criteria decision analysis for AI agents — score, rank & explain options.

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

endpoint
https://decisionmatrix-mcp.pages.dev/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
124ms

last good check

priced tools
0

of 6 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ used through this hub 30 days

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.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 6 tools
2 open 4 never probed 2 of 6 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.

  • health_check open 30m ago

    Server health, version, and capabilities. No parameters.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • list_methods open 30m ago

    List the available scoring methods (weighted_sum, weighted_product, topsis) with descriptions, normalization details, score ranges, and when to use each. No parameters.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • create_decision unknown never probed

    Rank named options against weighted criteria and return the winner, full ranking, per-criterion score breakdowns, methodology, the weights used, and a plain-language explanation. This is the main tool. Provide options, criteria [{name, weight, direction}], and a scores matrix. method defaults to weighted_sum (also: weighted_product, topsis). 100% deterministic.

    mcp-tool

    {
  "type": "object",
  "required": [
    "options",
    "criteria",
    "scores"
  ],
  "properties": {
    "method": {
      "enum": [
        "weighted_sum",
        "weighted_product",
        "topsis"
      ],
      "type": "string",
      "default": "weighted_sum"
    },
    "scores": {
      "type": "object",
      "description": "Score matrix. Object form: {\"Option A\": {\"Criterion 1\": 8, ...}, ...}. Array form: [{\"option\":\"Option A\",\"scores\":{...}}]. Or inline scores on each option object."
    },
    "options": {
      "type": "array",
      "items": {
        "type": [
          "string",
          "object"
        ]
      },
      "description": "Named alternatives. Strings [\"A\",\"B\"] or objects [{\"name\":\"A\",\"scores\":{...}}]."
    },
    "criteria": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "weight"
        ],
        "properties": {
          "name": {
            "type": "string"
          },
          "weight": {
            "type": "number"
          },
          "direction": {
            "enum": [
              "benefit",
              "cost"
            ],
            "type": "string",
            "default": "benefit"
          }
        }
      },
      "description": "Weighted criteria. Each: {name, weight (relative, >=0), direction: 'benefit' (higher better, default) | 'cost' (lower better)}."
    }
  }
}
    arguments 60 lines
  • score_options unknown never probed

    Score options against criteria when the score matrix is supplied separately. Returns the full normalized scored matrix (per-option, per-criterion) plus a ranking, without the narrative winner explanation. Use create_decision if you want a winner + explanation.

    mcp-tool

    {
  "type": "object",
  "required": [
    "options",
    "criteria",
    "scores"
  ],
  "properties": {
    "method": {
      "enum": [
        "weighted_sum",
        "weighted_product",
        "topsis"
      ],
      "type": "string",
      "default": "weighted_sum"
    },
    "scores": {
      "type": "object",
      "description": "Score matrix. Object form: {\"Option A\": {\"Criterion 1\": 8, ...}, ...}. Array form: [{\"option\":\"Option A\",\"scores\":{...}}]. Or inline scores on each option object."
    },
    "options": {
      "type": "array",
      "items": {
        "type": [
          "string",
          "object"
        ]
      },
      "description": "Named alternatives. Strings [\"A\",\"B\"] or objects [{\"name\":\"A\",\"scores\":{...}}]."
    },
    "criteria": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "weight"
        ],
        "properties": {
          "name": {
            "type": "string"
          },
          "weight": {
            "type": "number"
          },
          "direction": {
            "enum": [
              "benefit",
              "cost"
            ],
            "type": "string",
            "default": "benefit"
          }
        }
      },
      "description": "Weighted criteria. Each: {name, weight (relative, >=0), direction: 'benefit' (higher better, default) | 'cost' (lower better)}."
    }
  }
}
    arguments 60 lines
  • sensitivity_analysis unknown never probed

    Test how robust the winner is to changes in criteria weights. Sweeps each criterion's weight +/- 'variation' (default 0.2 = 20%) over 'steps' (default 10) increments, recomputes the ranking, and reports a robustness score, which criteria are most likely to flip the result, and the flip points.

    mcp-tool

    {
  "type": "object",
  "required": [
    "options",
    "criteria",
    "scores"
  ],
  "properties": {
    "steps": {
      "type": "integer",
      "default": 10,
      "description": "Number of weight steps per criterion (2-100)."
    },
    "method": {
      "enum": [
        "weighted_sum",
        "weighted_product",
        "topsis"
      ],
      "type": "string",
      "default": "weighted_sum"
    },
    "scores": {
      "type": "object",
      "description": "Score matrix. Object form: {\"Option A\": {\"Criterion 1\": 8, ...}, ...}. Array form: [{\"option\":\"Option A\",\"scores\":{...}}]. Or inline scores on each option object."
    },
    "options": {
      "type": "array",
      "items": {
        "type": [
          "string",
          "object"
        ]
      },
      "description": "Named alternatives. Strings [\"A\",\"B\"] or objects [{\"name\":\"A\",\"scores\":{...}}]."
    },
    "criteria": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "weight"
        ],
        "properties": {
          "name": {
            "type": "string"
          },
          "weight": {
            "type": "number"
          },
          "direction": {
            "enum": [
              "benefit",
              "cost"
            ],
            "type": "string",
            "default": "benefit"
          }
        }
      },
      "description": "Weighted criteria. Each: {name, weight (relative, >=0), direction: 'benefit' (higher better, default) | 'cost' (lower better)}."
    },
    "variation": {
      "type": "number",
      "default": 0.2,
      "description": "Fractional weight sweep, 0<v<=1. 0.2 = +/-20%."
    }
  }
}
    arguments 70 lines
  • compare_two unknown never probed

    Direct head-to-head comparison of exactly two options. Returns the winner, the score margin, how many criteria each option wins, and a per-criterion breakdown of who each criterion favours. Pass option_a and option_b (names) or a 2-element options array, plus criteria and scores.

    mcp-tool

    {
  "type": "object",
  "required": [
    "criteria",
    "scores"
  ],
  "properties": {
    "method": {
      "enum": [
        "weighted_sum",
        "weighted_product",
        "topsis"
      ],
      "type": "string",
      "default": "weighted_sum"
    },
    "scores": {
      "type": "object",
      "description": "Score matrix. Object form: {\"Option A\": {\"Criterion 1\": 8, ...}, ...}. Array form: [{\"option\":\"Option A\",\"scores\":{...}}]. Or inline scores on each option object."
    },
    "options": {
      "type": "array",
      "items": {
        "type": [
          "string",
          "object"
        ]
      },
      "description": "Named alternatives. Strings [\"A\",\"B\"] or objects [{\"name\":\"A\",\"scores\":{...}}]."
    },
    "criteria": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "name",
          "weight"
        ],
        "properties": {
          "name": {
            "type": "string"
          },
          "weight": {
            "type": "number"
          },
          "direction": {
            "enum": [
              "benefit",
              "cost"
            ],
            "type": "string",
            "default": "benefit"
          }
        }
      },
      "description": "Weighted criteria. Each: {name, weight (relative, >=0), direction: 'benefit' (higher better, default) | 'cost' (lower better)}."
    },
    "option_a": {
      "type": "string"
    },
    "option_b": {
      "type": "string"
    }
  }
}
    arguments 65 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.

_ for your README measured, not declared

measured by brick.blue

[![measured by brick.blue](https://brick.blue/api/v1/agents/460a6f6f1c954c6d/badge.svg)](https://brick.blue/agent/460a6f6f1c954c6d)

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.

_ 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
0

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
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settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
0
rate
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reviews
paid reviews
0
positive
0
negative
0
score
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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.