FMP MCP Server

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

The description adds valuable behavioral context beyond annotations: it specifies the tool analyzes 'the most recent (or specified) earnings report' and details the return data (e.g., 'post-earnings price reaction', 'guidance tone from transcript'). Annotations cover safety (readOnlyHint, non-destructive) and idempotency, but the description enriches this with operational scope and output specifics, 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 appropriately sized and front-loaded: the first sentence succinctly lists key analysis areas, followed by a clear purpose statement and detailed return values. The parameter explanations are concise and necessary. Every sentence adds value without redundancy, making it easy to scan and understand.

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 (analyzing earnings reports with multiple facets), the description is complete: it covers purpose, usage, parameters, and behavioral traits. With annotations providing safety hints and an output schema presumably detailing return values, the description fills gaps effectively, ensuring the agent has sufficient context 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 compensates well by explaining parameter semantics: 'symbol' is defined as 'Stock ticker symbol', 'quarter' as 'Specific quarter (1-4) to analyze' with omission rules, and 'year' similarly. It adds meaning beyond the bare schema, though it could detail format constraints (e.g., symbol case-sensitivity) to reach a perfect score.

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 ('analyzes', 'returns') and resources ('earnings report'), listing the exact components it examines (EPS/revenue surprise, YoY/QoQ comparisons, price reaction, analyst rating changes, guidance tone). It distinguishes itself from siblings like 'earnings_info' or 'earnings_transcript' by focusing on post-earnings synthesis rather than raw data or transcripts.

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 provides clear context for when to use this tool ('Analyzes the most recent (or specified) earnings report'), but does not explicitly state when not to use it or name alternatives among siblings. It implies usage for post-earnings analysis rather than pre-earnings or general financial data, which is helpful but not exhaustive.

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