mcp-agent-reliability

score_tool_description

Score how clear, specific, and LLM-friendly a tool description is (0-100) before adding it to an agent to reduce wrong tool calls.

Instructions

Score how clear, specific and LLM-friendly a tool description is (0-100). Use this before adding a new tool to an agent to reduce wrong tool calls.

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": {
      "description": "<description>"
    },
    "name": "score_tool_description"
  }
}

Input Schema

Name	Required	Description	Default
`name`	No	Optional name of the tool (e.g. create_invoice)
`description`	Yes	The full tool description text to score

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 31, 2026

TDQS

A3.8/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It states the scoring criteria (clarity, specificity, LLM-friendliness) but does not disclose behavior such as whether the tool is read-only, what output format to expect, or any limitations. For an apparently safe utility, this lack of explicit safety disclosure is a notable gap.

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 sentences, front-loaded with the primary purpose, and every word contributes value. There is no redundancy or filler.

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?

For a simple tool with two parameters and no output schema, the description adequately explains the core function and a primary use case. Missing return-value details are acceptable given the tool's simplicity, but a bit more context on how to interpret the 0-100 score would have made it fully complete.

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 baseline is 3. The description text adds no additional parameter-level meaning beyond what the schema already provides for 'name' and 'description'. It does not compensate further, but it doesn't need to at this coverage level.

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's function: scoring how clear, specific, and LLM-friendly a tool description is on a 0-100 scale. It uses a specific verb (score) and resource (tool description), distinguishing it from sibling tools like estimate_token_cost or simulate_tool_choice.

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 provides a clear when-to-use instruction: 'Use this before adding a new tool to an agent to reduce wrong tool calls.' It doesn't explicitly mention alternatives or exclusions, but the context is specific enough to guide the agent.

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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