Skip to main content
Glama

analyze_image

Read-onlyIdempotent

Extract data from graphs, package drawings, pin diagrams, and schematics in component datasheets using vision AI. Provide a specific question to focus analysis.

Instructions

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
Behavior5/5

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

Annotations already declare safe read-only behavior, but the description adds valuable context: uses vision AI, extracts from graphs/drawings, and warns about reliability. No contradiction.

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?

Well-structured with front-loaded purpose, usage, and examples. The IMPORTANT section is valuable. Slightly verbose but still efficient.

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

Completeness3/5

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

No output schema is provided, so the description should clarify the return format. It implies output via examples but lacks explicit specification of response structure, leaving some gap.

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 descriptions cover 100% of parameters, so baseline is 3. The description adds meaning by explaining image_key origin and giving concrete examples, enhancing usability.

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

Purpose4/5

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

The description specifies the tool analyzes datasheet images using vision AI, with a clear verb and resource. While it distinguishes from read_datasheet, it does not contrast with all sibling tools, so it falls short of a 5.

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?

Explicit when-to-use (when read_datasheet returns images) and when-not-to-use (prefer text tables for precise values), plus examples and alternatives (read_datasheet). This is exemplary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/octoco-ltd/sheetsdata-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server