yf-decide-mcp

decide

Submit typed choice, score, yes/no probability, or region questions about supplied context to get structured decision answers with confidence.

Instructions

Ask one or more typed questions about a piece of context through a System One decision endpoint (POST /v1/systemone). This is a decision model, not a chat model: it does not generate prose, it answers the questions you define.

Question types:

choice: pick exactly one label from criteria (an object mapping label -> description).
score: position on 2-10 ordered levels given as an array, 0-based. Returns score, legend, probabilities and confidence.
noul: probability of yes, 0-1. criteria may optionally describe {true, false}. A value near 0.5 means uncertainty about yes/no, not medium intensity.
box: region of interest as {top,bottom,left,right} normalized 0-1.

Batch independent questions in one call; questions cannot see each other's answers. The model cannot see your conversation or local files — put every fact it needs into state, and write each full judgment into instructions. Question IDs are only response keys.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "state": "<state>",
      "questions": "<questions>"
    },
    "name": "decide"
  }
}

Input Schema

Name	Required	Description
`model`	No	Optional model override for this call. Falls back to MODEL.
`state`	Yes	Source text or JSON context the questions are about. Include all evidence needed.
`questions`	Yes	Named independent questions over the same state.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Nov 5, 2026

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden and does substantial work: it discloses that the model cannot see the conversation or local files, that questions are evaluated independently, and that question IDs are only response keys. It omits operational traits like authentication, latency, rate limits, and error behavior, and only describes the return shape for the score type.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core identity and the chat-model distinction, then uses a compact list keyed to the four question types, so it is scannable rather than dense. A few clauses, such as restating that question IDs are only response keys, sit slightly apart from the main flow but still carry real information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a nested, three-parameter tool with no output schema, the description supplies enough to call it correctly: what state must contain, how criteria vary by type, and how batching behaves. The main gap is return-value coverage, since only the score type's response fields are described, leaving choice, noul, and box outputs unspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so a baseline of 3 applies, and the description goes beyond it by explaining what each question type expects in criteria (label->description map, 2-10 ordered levels, optional {true,false}, omitted for box) and by stating the intent of state and instructions. The model override parameter is not mentioned, but its schema description already covers the fallback behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action (ask typed questions about a piece of context) against a named endpoint (POST /v1/systemone) and explicitly contrasts it with what an agent might otherwise assume: it is a decision model, not a chat model, and returns answers rather than prose. That contrast alone lets an agent tell which kind of tool this is without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives concrete operating guidance: batch independent questions in one call, questions cannot see each other's answers, put every needed fact into state, and write each full judgment into instructions. There is no explicit when-not-to-use rule beyond the chat-model contrast, and no sibling tools exist to route against, so a 4 rather than 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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