FMP MCP Server

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

The description adds valuable behavioral context beyond annotations by revealing it 'Orchestrates 8 endpoints' and returns multiple data points (days until earnings, consensus EPS/revenue, etc.). While annotations cover read-only, non-destructive, idempotent, and open-world aspects, the description enhances understanding of the tool's comprehensive data aggregation behavior without contradicting 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 efficiently structured with a clear purpose statement upfront, followed by implementation details and parameter explanation. Every sentence adds value: the first defines scope, the second explains orchestration and outputs, and the third clarifies the parameter. No wasted words.

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 (orchestrating 8 endpoints), rich annotations, and the presence of an output schema, the description is complete. It explains the tool's purpose, usage context, behavioral scope, and parameter meaning without needing to detail return values (handled by output schema) or repeat annotation information.

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 for the single parameter 'symbol', the description compensates by providing a clear example ('e.g. "AAPL", "DDOG"') and context that it's a stock ticker symbol for earnings analysis. This adds meaningful semantics beyond the bare schema, though it doesn't detail format constraints like length or valid characters.

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 ('Pre-earnings positioning analysis') and resources ('consensus estimates, historical beat/miss rate, analyst momentum, price drift, and insider signals'), distinguishing it from siblings like earnings_info or earnings_postmortem by focusing on pre-earnings setup analysis rather than general earnings data or post-event analysis.

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 when to use this tool: to answer 'should I play this earnings?' This provides clear context for pre-earnings decision-making and implicitly distinguishes it from alternatives like earnings_info (general earnings data) or earnings_postmortem (post-event analysis), though it doesn't name specific exclusions.

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