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kenpom

kenpom

by omalleyandy
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

classify_effort

Determines the appropriate reasoning depth by classifying query effort levels into low, medium, or high, with model hints and thinking budget for efficient task execution.

Instructions

Classify a query's effort level for dynamic model routing. Returns recommended effort level (low/medium/high), model hint, and thinking budget. Use this to determine appropriate reasoning depth before executing complex tasks.

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": {
      "query": "<query>"
    },
    "name": "classify_effort"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesThe user query or task description to classify

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the return values (effort level, model hint, thinking budget) but does not mention any side effects, failure modes, or limitations. As a classification tool, it is likely read-only, but the description does not explicitly confirm this.

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 consists of two concise sentences: one stating the function and return values, the second giving usage context. Every word adds value with no redundancy or fluff.

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

Completeness3/5

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

Given the simplicity (one parameter, no output schema, no annotations), the description explains the purpose and output. However, it could be more complete by explaining what the effort levels (low/medium/high) imply or how the model hint should be used. Nonetheless, it is minimally adequate.

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?

There is only one parameter ('query') with 100% schema description coverage. The description in the tool definition ('The user query or task description to classify') adds marginal value beyond the schema's own description ('The user query or task description to classify'). Baseline 3 is appropriate.

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 clearly states the tool classifies a query's effort level for dynamic model routing, specifying the verb 'classify' and the resource 'query's effort level'. It distinguishes itself by mentioning the return values (effort level, model hint, thinking budget), which likely differentiates it from sibling tools like 'get_tool_effort'.

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?

The description explicitly says to use this tool 'to determine appropriate reasoning depth before executing complex tasks.' This provides clear context. While it does not elaborate on when not to use it or mention alternatives, the usage guidance is sufficient for an agent to decide.

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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