FMP MCP Server

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 useful context about what the tool does (compares specific metrics, shows premium/discount and rank) and mentions 'FMP-identified peers' as the source, but doesn't disclose rate limits, authentication needs, or detailed behavioral traits beyond the annotations.

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 front-loaded with the core purpose in the first sentence, followed by specific metrics and output details. The Args section is brief and directly relevant. Every sentence adds value without redundancy, making it efficient and well-structured.

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 (peer comparison with multiple metrics), rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is complete enough. It explains the purpose, parameters, and what to expect in results, leaving no significant gaps for an AI agent.

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 and only one parameter, the description compensates by explaining the 'symbol' parameter as 'Stock ticker symbol (e.g., "AAPL")' and providing an example. This adds clear meaning beyond the bare schema, though it doesn't detail format constraints or validation rules.

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 specific action ('Compare a stock's valuation and growth metrics against its peers') and identifies the resource ('stock'). It distinguishes from siblings by focusing on peer comparison rather than individual company analysis (e.g., company_overview) or market-wide data (e.g., market_overview).

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 implies usage context by specifying it compares against 'FMP-identified peers' and focuses on valuation/growth metrics, suggesting it's for relative analysis. However, it lacks explicit guidance on when to use this tool versus alternatives like analyst_consensus or fair_value_estimate, and doesn't state any exclusions or prerequisites.

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