Financial Modeling Prep MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions using the 'FMP Stock Peer Comparison API' and the goal of gaining insights, but doesn't describe key behavioral traits such as whether this is a read-only operation, what the output format looks like (e.g., list of peers with metrics), potential rate limits, error conditions, or authentication requirements. For a tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 concise and well-structured in two sentences. The first sentence clearly states the purpose and method, while the second explains the benefit. There's no wasted language, and it's front-loaded with the core functionality. However, it could be slightly more efficient by integrating the insight gain into the first sentence.

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 (peer comparison with sector and market cap filtering), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the output contains (e.g., list of peer symbols, comparative metrics), how results are filtered or sorted, or any limitations (e.g., data freshness, supported exchanges). For a tool that likely returns structured peer data, this omission makes it inadequate for an agent to fully understand the tool's context.

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 100% description coverage, with the single parameter 'symbol' documented as 'Stock symbol'. The description doesn't add any additional semantic context beyond this—it doesn't clarify format (e.g., ticker case-sensitivity), examples, or constraints. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema already provides adequate parameter documentation without extra value from the 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: 'Identify and compare companies within the same sector and market capitalization range' using a specific API. It specifies the verb ('identify and compare'), resource ('companies'), and scope ('same sector and market capitalization range'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'getStockPeersBulk' or 'searchCompaniesBySymbol', which could provide similar peer-related functionality.

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 minimal usage guidance. It mentions gaining insights into how a company compares to peers, but doesn't specify when to use this tool versus alternatives like 'getStockPeersBulk' (for multiple symbols) or other comparison tools in the sibling list. There's no explicit when-to-use or when-not-to-use context, leaving the agent to infer usage based on the purpose alone.

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