SheetsData MCP

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

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it clarifies reliability limitations ('vision-extracted graph data' vs. 'more reliable' text tables) and specifies use cases for visual information not in text. No contradiction with annotations exists.

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

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

The description is well-structured and front-loaded with the core purpose and usage guidelines. Every sentence adds value: it explains when to use the tool, provides important caveats, and includes practical examples without redundancy. The information is dense but efficiently presented.

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

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

Given the tool's complexity (vision AI analysis), rich annotations (covering safety and behavior), and 100% schema coverage, the description is complete. It addresses key contextual elements: integration with sibling tools (read_datasheet), reliability considerations, and specific use cases. Although there's no output schema, the examples imply the return format, and the description compensates adequately.

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

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

Schema description coverage is 100%, providing clear descriptions for all parameters. The description adds some semantic context: it explains that 'image_key' comes from 'read_datasheet response' and gives examples of usage, but doesn't significantly enhance meaning beyond the schema. This meets the baseline of 3 for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Analyze an image from a component's datasheet using vision AI' with specific resources (images from datasheets) and actions (extract data from graphs, drawings, diagrams, schematics). It distinguishes itself from sibling tools like 'read_datasheet' by focusing on visual analysis rather than text extraction.

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

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

The description provides explicit guidance on when to use this tool: '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.' It also specifies when not to use it: 'For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first' and offers clear alternatives (read_datasheet for text tables).

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