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said-done

said-done

by Jimmy2004
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Python
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test_classifier

Classifies built-in synthetic input to validate classification behavior without touching a user's pending transcript.

Instructions

Classify built-in synthetic input, never a user's pending transcript.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "test_classifier"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description still discloses an important behavioral boundary: it operates on built-in synthetic input and not on pending user transcripts. However, it does not say whether the operation is read-only, whether it mutates classifier state, or what the response contains.

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?

A single sentence with no wasted wording. The synthetic-input scope is front-loaded, and the exclusion of pending transcripts is stated immediately after.

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?

For a zero-parameter test tool, the description identifies the input source but omits what the test validates, whether the current classifier configuration is used, and what an agent should expect back. With no annotations and no output schema, those gaps leave the agent with only a partial behavioral picture.

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?

The tool has zero parameters and schema coverage is 100%, so there is no parameter syntax the description needs to compensate for. Baseline 4 applies because the schema fully covers the empty argument object.

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 ("Classify") and resource ("built-in synthetic input"), and explicitly distinguishes this from classifying a user's pending transcript. It does not explain what the classification is for or what it validates, but the core action is unambiguous.

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 exclusion "never a user's pending transcript" implies this is for testing rather than real transcript processing, but it does not name the alternative tools such as retry_classification or process_now. Usage is inferable, not explicit.

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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