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fusion-cad-ai

fusion-cad-ai

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Python

inspect_part

Read-onlyIdempotent

Verify a part's parameters against an expected JSON specification, returning PASS or FAIL to confirm geometry matches design intent.

Instructions

Сверка с expected_json: PASS или FAIL.

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": "inspect_part"
  }
}

Input Schema

NameRequiredDescriptionDefault
object_nameNo
expected_jsonNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds that the result is a PASS/FAIL verdict, which is genuine output-shape context beyond the annotations, but it says nothing about what is compared, tolerances, or failure modes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single short sentence with zero filler and the outcome format front-loaded. It is efficient, though its brevity borders on under-specification.

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?

For a two-parameter tool with no output schema and no schema descriptions, the definition should explain what object_name identifies, what shape expected_json takes, and what a FAIL means. None of that is present, leaving the agent unable to call the tool confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description names only expected_json, with no format or schema for that JSON. object_name, the other parameter, is never mentioned at all, so the description fails to compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the comparison target (expected_json) and the outcome format (PASS/FAIL), but never says what is being inspected or against what fixture. In a sibling set containing validate, measure, and design_audit, the agent cannot tell what 'inspect_part' actually checks, so the purpose reads as a partial restatement of the name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use, when-not-to-use, or alternative routing. Nothing distinguishes this from validate or design_audit, which an agent would plausibly consider for similar verification tasks.

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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