FMP MCP Server

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

Annotations already provide excellent behavioral coverage (readOnlyHint=true, destructiveHint=false, openWorldHint=true, idempotentHint=true). The description adds valuable context by specifying the return format ('top 10 holders with % ownership, quarter-over-quarter position changes, and institutional vs float ratio'), which helps the agent understand what data to expect. 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 perfectly structured and concise: a clear purpose statement, specific return details, and parameter explanation in just three lines. Every sentence earns its place, with no redundant information. The front-loaded purpose statement immediately communicates the tool's function.

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 (covering safety and behavior), and the existence of an output schema (which handles return value documentation), the description is complete enough. It explains the purpose, return data format, and parameter usage, providing all necessary context for the agent to use the tool effectively without over-explaining.

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 single parameter's purpose and format ('Stock ticker symbol (e.g. "AAPL")'). This adds essential meaning beyond the bare schema, making the parameter's usage clear despite the schema's lack of description.

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 institutional ownership breakdown and position changes') and identifies the resource (stock ownership data). It distinguishes from siblings by focusing on institutional ownership rather than earnings, dividends, or other financial metrics. However, it doesn't explicitly contrast with similar tools like 'insider_activity' or '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 through the parameter description (stock ticker symbol) and the mention of specific data returned. However, it provides no explicit guidance on when to use this tool versus alternatives like 'insider_activity' for ownership changes or 'company_overview' for broader company data. The agent must infer appropriate usage from the tool's name and description alone.

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