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gutfeel-mcp

gutfeel-mcp

by Kungie
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

classify

Read-onlyIdempotent

Assign an appropriate label to any text, such as team, topic, language, or intent, and return each option's probability plus a confidence score.

Instructions

Pick the option that fits a text best: a team, a topic, a language, an intent. Answers with the chosen label, every option's probability 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>",
      "options": "<options>"
    },
    "name": "classify"
  }
}

Input Schema

NameRequiredDescriptionDefault
stakesNoHow sure the model must be before an answer counts. Needs ask_human.
optionsYesThe categories: {label: description}, or a list of labels. Include a catch-all such as "other", or every text is forced into one of them.
subjectYesThe text to judge: an email, a comment, a diff.
questionNoWhat to decide, if the options 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

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and closed-world behavior, so the bar is lower. The description adds genuine behavioral value by disclosing the return shape (chosen label, per-option probabilities, confidence), which the annotations do not cover.

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?

Two tightly written sentences with no filler; the core action is front-loaded and the output behavior follows. Every clause earns its place.

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?

An output schema exists, so return values need not be re-explained, and all 5 parameters carry full schema descriptions. The description is complete enough to invoke the tool correctly, though it could note the stakes/ask_human interaction more explicitly.

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 coverage is 100%, so the baseline is 3, but the description goes further by illustrating what 'options' actually look like in practice (team/topic/language/intent), giving the agent a concrete model of the parameter beyond the schema's structural typing.

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?

States a specific verb (pick/classify) and resource (a text against options), with concrete examples of option kinds (team, topic, language, intent). It is clear what the tool does, but it makes no attempt to distinguish itself from siblings like likely, rate, or each.

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?

Usage is implied by the description — classify a text into the provided option set — but there is no explicit when-to-use, when-not-to-use, or routing away from sibling tools such as rate or likely. The agent must infer the selection criteria.

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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