FMP MCP Server

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context by detailing the multi-method approach (PE-based, PS-based, simplified DCF, analyst target) and peer comparison, which goes beyond annotations to explain how the tool operates and what it returns.

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 well-structured and front-loaded with the core purpose, followed by method details and return values. Every sentence adds value without redundancy, and the parameter explanation is concise 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 complexity (multi-method valuation), rich annotations, and the presence of an output schema, the description is complete enough. It explains the calculation methods, peer context, and return types, leaving output details to the 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 compensates by explaining the 'symbol' parameter as a stock ticker with an example ('AAPL'), adding meaning beyond the bare schema. However, 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 ('calculates', 'compares', 'returns') and resources (fair value using multiple methods, peer medians, blended fair value, upside/downside, valuation rating). It distinguishes from siblings by focusing on valuation estimation rather than consensus, overview, or other financial data.

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 for valuation analysis but doesn't explicitly state when to use this tool versus alternatives like 'analyst_consensus' or 'peer_comparison'. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool's name and description.

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