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googoles

Parts Finder MCP

by googoles

extract_visual_part_hints

Converts visual observations from image recognition into structured part hints for searching. Supply recognized text, shape, pin counts, colors, and dimensions to generate searchable specifications.

Instructions

Normalize image-recognition observations into searchable part hints. The MCP server does not process raw images; pass observations from Codex/Claude vision.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
colorNo
notesNo
pinCountNo
userGoalNo
pinLayoutNo
confidenceNo
motorHintsNo
visibleTextNo
boardContextNo
dimensionsMmNo
imageQualityNo
packageShapeNo
cableWireCountNo
connectorPitchMmNo
connectorPinCountNo
Behavior2/5

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

No annotations provided; the description only mentions that raw images are not processed, but fails to disclose behavioral traits such as side effects, rate limits, or output format.

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?

The description is concise with two front-loaded sentences, each earning its place by clarifying purpose and constraints, though it sacrifices parameter details.

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

Completeness1/5

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

Given 15 parameters, nested objects, no output schema, and no annotations, the description is severely incomplete, lacking return value info and parameter guidance.

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

Parameters1/5

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

With 0% schema description coverage and no parameter explanations in the description, the AI agent receives no additional meaning beyond parameter names, which is insufficient for a tool with 15 complex parameters.

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 normalizes image-recognition observations into searchable part hints, distinguishing it from siblings like search_parts (text-based) and compare_parts.

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 description implies usage by requiring observations from Codex/Claude vision, but does not explicitly specify when to use this tool versus alternatives, nor does it list exclusions.

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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