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 the API provides 'essential dividend data' but fails to detail key behaviors: it does not specify if this is a read-only operation, the data format returned, potential rate limits, authentication requirements, or error handling. For a tool with no annotations, this is a significant gap in transparency.

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 front-loaded, stating the purpose in the first sentence and elaborating slightly in the second. There is no wasted language, and it efficiently communicates the core function. However, it could be slightly more structured by explicitly separating purpose from usage context, preventing a perfect score.

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 complexity of financial data tools and the lack of annotations and output schema, the description is incomplete. It does not explain what the return values look like (e.g., JSON structure, data fields), potential limitations, or how to handle the 'limit' parameter effectively. For a tool with no structured output information, this leaves significant gaps for an AI agent.

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, clearly documenting the 'symbol' and 'limit' parameters. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or providing examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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: to provide dividend data for individual stock symbols, including specific data points like record dates, payment dates, and declaration dates. It uses specific verbs ('stay informed,' 'provides') and identifies the resource ('dividend data for individual stock symbols'), but it does not explicitly differentiate from sibling tools like 'getDividendsCalendar' or 'getStockSplitCalendar,' which prevents a score of 5.

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 offers no guidance on when to use this tool versus alternatives. It mentions staying informed about upcoming dividend payments but does not specify prerequisites, exclusions, or compare it to related tools such as 'getDividendsCalendar' or 'getStockSplits.' This lack of contextual usage information limits its effectiveness for an AI agent.

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