FMP MCP Server

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

The description adds valuable context beyond annotations by specifying the comprehensive nature of the snapshot and listing example data fields (name, sector, market cap, etc.). While annotations cover safety (readOnlyHint, non-destructive) and reliability (idempotent, openWorld), the description enhances understanding of what information will be returned, though it doesn't mention rate limits or authentication needs.

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 efficiently structured: first sentence states the purpose, second provides usage guidance, third lists return fields, and fourth explains the parameter. Every sentence adds value without redundancy, and key information is front-loaded. The Args section is appropriately brief and clear.

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 moderate complexity, rich annotations (readOnlyHint, idempotentHint, etc.), and the presence of an output schema, the description is complete. It explains the tool's role, when to use it, what data it returns, and parameter semantics, providing sufficient context for an agent to invoke it correctly without needing to detail return values (handled by output schema).

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?

With 0% schema description coverage, the description fully compensates by explaining the 'symbol' parameter as 'Stock ticker symbol (e.g. "AAPL", "MSFT")'. This provides clear semantics and examples, making the parameter meaning explicit despite the schema lacking descriptions. The description effectively handles the single parameter's purpose.

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 with specific verbs ('Get a comprehensive company snapshot') and resources ('company snapshot including profile, valuation ratios, and current quote'). It distinguishes itself from siblings by providing a broad overview rather than specialized data like earnings or dividends, making it immediately clear what this tool does differently.

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 explicitly states 'Use this FIRST for any stock analysis query,' providing clear guidance on when to use this tool versus alternatives. This directive helps the agent prioritize this tool for initial company assessments over more specialized siblings like analyst_consensus or financial_statements.

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