mcp-agent-reliability

simulate_tool_choice

Predict which tool an agent will choose from a given list based on a user prompt, enabling testing of tool selection before production.

Instructions

Given a user prompt and a list of available tools, predict which tool an agent is most likely to pick. Useful for testing tool selection before production.

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": {
      "prompt": "<prompt>",
      "tools": "<tools>"
    },
    "name": "simulate_tool_choice"
  }
}

Input Schema

Name	Required	Description	Default
`tools`	Yes	List of tools the agent can choose from
`prompt`	Yes	The user message or task the agent will see

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 31, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are present, so the description must disclose behavioral traits. It only states what the tool does, not whether it is read-only, deterministic, or how predictions are generated. Missing important behavioral context for a simulation tool.

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 with no fluff. The first sentence states the core function, the second gives a use case. Efficient and appropriately sized.

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

Completeness2/5

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

The description omits expected output format or any caveats about the prediction (e.g., confidence score, top-n tools). With no output schema, this is a significant gap for a prediction tool, making it incomplete for practical use.

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 coverage is 100%, so the baseline is 3. The description mentions 'user prompt' and 'list of available tools' but adds no extra detail beyond the schema's own parameter descriptions.

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?

Clearly states it predicts which tool an agent is most likely to pick given a prompt and tool list. The verb 'predict' and resource 'tool choice' are specific, and this distinguishes it from siblings that score descriptions or generate tests.

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?

Provides a clear use context: 'testing tool selection before production.' However, it does not explicitly mention alternatives or when not to use the tool, so it falls short of a full 'when/when-not' guide.

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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