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gutfeel-mcp

gutfeel-mcp

by Kungie
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

each

Read-onlyIdempotent

Ask one question about many texts at once for yes/no, classification, or rating in a single call, avoiding separate requests.

Instructions

Ask the same question about many texts at once: likely with just a question, classify with options, rate with levels. Far faster than one call per text.

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": {
      "subjects": "<subjects>"
    },
    "name": "each"
  }
}

Input Schema

NameRequiredDescriptionDefault
levelsNoA scale, as for rate.
stakesNoHow sure the model must be before an answer counts. Needs ask_human.
optionsNoCategories, as for classify.
questionNoA claim, as for likely. Alone, it asks likely; with options or levels, it says what to decide.
subjectsYesThe texts to judge, all with the same question.
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

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, and closed-world, so the safety profile is covered. The description adds a performance claim ('far faster'), which is useful context, but says nothing about how batch failures or per-item uncertainty behave — the stakes/ask_human interaction is only in the schema.

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 sentences, zero filler. The mode mapping comes first and the efficiency argument closes it out; every clause carries 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?

With an output schema present, return values need no explanation, and all six params are documented in the schema. The only real gap is whether results are returned in subjects order and how per-item errors surface, which the description doesn't touch.

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 the schema already explains subjects, question, options, levels, stakes, and ask_human. The description restates the question/options/levels relationship in prose without adding syntax or format detail beyond the schema, so the baseline 3 applies.

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 operation (ask the same question about many texts) and maps it onto the three sibling modes (likely/classify/rate) via 'likely with just a question, classify with options, rate with levels.' The name 'each' alone is opaque, but the text rescues it and an agent can distinguish this batch tool from the single-item siblings.

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 clear selection logic via the question/options/levels mapping and a reason to prefer it ('Far faster than one call per text'). It does not explicitly say to fall back to likely/classify/rate for a single text, so the when-not is left to inference.

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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