FMP MCP Server

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

Annotations already provide key behavioral hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true), but the description adds valuable context by detailing what specific data is returned (e.g., net buy/sell over 30/90 days, cluster buying signals). It doesn't contradict annotations and enhances understanding of the tool's output scope beyond the basic safety profile.

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, followed by specific return details and parameter explanation in a structured 'Args:' section. Every sentence adds value without redundancy, making it efficient and easy to parse.

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, and the presence of an output schema, the description is complete enough. It clearly states what the tool does, what it returns, and the required parameter, leaving detailed output structure to the schema. No critical gaps exist for effective agent use.

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?

The input schema has 0% description coverage, but the description compensates by explaining the 'symbol' parameter with an example ('AAPL') and clarifying it's a stock ticker symbol. This adds meaningful semantics 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 tool's purpose with specific verbs ('Get insider trading activity, statistics, and ownership context') and distinguishes it from siblings by focusing on insider trading data rather than analyst consensus, earnings, or other financial metrics. It explicitly lists the types of data returned (net buy/sell, cluster buying signals, CEO/CFO action highlights, ownership percentage).

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 returns insider trading data for a stock symbol, but it doesn't explicitly state when to use this tool versus alternatives like 'institutional_ownership' or 'stock_brief'. No exclusions or clear alternatives are mentioned, leaving some ambiguity about optimal use cases.

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