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gutfeel-mcp

gutfeel-mcp

by Kungie
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

rate

Read-onlyIdempotent

Score a text on an ordered scale such as urgency or severity. Returns a numeric score, nearest level, and confidence.

Instructions

Rate a text on an ordered scale such as urgency or severity. Answers with a score, which can fall between levels, the nearest level and a confidence.

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": {
      "subject": "<subject>",
      "levels": "<levels>"
    },
    "name": "rate"
  }
}

Input Schema

NameRequiredDescriptionDefault
levelsYesBetween 2 and 10 level descriptions, in order; the first is level 0.
stakesNoHow sure the model must be before an answer counts. Needs ask_human.
subjectYesThe text to judge: an email, a comment, a diff.
questionNoWhat to rate, if the levels do not say it.
ask_humanNoAllow the outcome `unsure` when the model cannot tell. Off by default.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety and side-effect profile is covered. The description adds output behavior by saying answers can fall between levels and include a nearest level and confidence, but it does not mention the schema-level unsure outcome enabled by ask_human or the effect of stakes.

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

Conciseness5/5

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

The description is two efficient sentences with no filler. The purpose is front-loaded, followed immediately by the output shape, making it easy for an agent to scan.

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?

Given the rich schema, output schema, and read-only/idempotent annotations, the description is nearly complete for invocation. The main remaining gap is sibling-relative guidance for choosing rate over classify, likely, or each.

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

Parameters3/5

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

Schema description coverage is 100%, so all five parameters are already documented in the schema. The description adds only the general notion of an ordered scale and does not provide additional parameter-level meaning beyond what the schema already supplies.

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

Purpose4/5

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

The description states a specific verb and resource: 'Rate a text on an ordered scale such as urgency or severity.' It also characterizes the distinctive output ('a score, which can fall between levels, the nearest level and a confidence'), which helps separate it from categorical siblings like classify, though it does not name an alternative directly.

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

Usage Guidelines3/5

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

The phrase 'on an ordered scale such as urgency or severity' implies the use case and distinguishes it somewhat from discrete classification, but there is no explicit when-to-use or when-not-to-use guidance relative to siblings such as classify, likely, or each.

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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