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sheetsdata-mcp

by io.github.octoco-ltd

analyze_image

Read-onlyIdempotent

Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis.

IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves.

Examples:

  • analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image

  • analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionNoOptional specific question about the image (e.g. 'What are the package dimensions?')
image_keyYesImage storage path from read_datasheet output (e.g. 'images/abc123.png')
part_numberYesMPN of the component

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, providing the base safety profile. The description adds valuable context about the reliability of vision-extracted data versus text tables and explains the dependency on image_key from read_datasheet, which are behavioral traits not covered by annotations.

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 well-structured: it front-loads the purpose, then provides usage guidance, an important caveat, and examples. It is slightly long but every sentence contributes value, and the formatting with an IMPORTANT section and examples improves scannability.

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?

Given there is no output schema, the description adequately conveys what the tool returns through examples (e.g., classifies and describes the image). It also covers the workflow dependency on read_datasheet and the trade-offs between vision and text extraction, making it sufficiently complete for correct invocation.

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?

Schema coverage is 100%, so parameter basics are already documented. The description enhances this with usage examples showing how image_key and question interact, and clarifies that image_key is the storage path from read_datasheet output, adding relational meaning beyond the schema.

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 analyzes images from datasheets using vision AI, with a specific verb ('Analyze') and resource (image from a component's datasheet). It distinguishes itself from read_datasheet by specifying it handles visual data like graphs, package drawings, pin diagrams, and circuit schematics.

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

Usage Guidelines5/5

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

The description explicitly says when to use this tool (when read_datasheet returns images needing extraction) and when not to use it (for precise numeric values, where read_datasheet text tables are preferred). It names the alternative tool, read_datasheet, and provides concrete examples of appropriate use cases.

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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TDQS

A4.8/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: search_parts finds parts, search_datasheets searches extracted content, read_datasheet reads sections, analyze_image extracts visual data, compare_parts batches comparisons, find_alternative finds substitutes, check_design_fit validates specs, prefetch/check/manage extraction status, and request/confirm handle uploads. The descriptions cross-reference each other to further eliminate ambiguity.

Naming Consistency5/5

All 12 tools follow the verb_noun snake_case pattern consistently (analyze_image, check_design_fit, compare_parts, prefetch_datasheets, request_datasheet_upload, etc.). No mixing of camelCase, verb styles, or vague generic names.

Tool Count5/5

12 tools is ideal for a datasheet-centric server, covering the full workflow from part discovery and datasheet extraction to analysis, validation, comparison, and upload. The count is neither bloated nor thin, and every tool earns its place.

Completeness5/5

The tool surface is comprehensive for the domain: part search, spec-based semantic search, detailed part data, datasheet reading (section/search), image analysis, design-fit checking, part comparison, alternative finding, extraction lifecycle management, and manual datasheet upload (request+confirm). No obvious dead ends or critical missing operations.

Resources