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 accessing data via 'The FMP Company Historical Employee Count API' but does not cover critical aspects like rate limits, authentication requirements, error handling, or the format of returned data. For a tool with no annotations, this leaves significant gaps in understanding its operational behavior.

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 states the core functionality, and the second elaborates on the value. There is no redundant information, and it is front-loaded with the main purpose. However, it could be slightly more direct by integrating usage context.

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 historical data retrieval, lack of annotations, and no output schema, the description is incomplete. It fails to address key contextual elements such as data format, time periods covered, potential limitations, or how results are structured. This makes it inadequate for an AI agent to fully understand the tool's behavior and outputs.

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 'symbol' as 'Stock symbol' and 'limit' with its default and max values. The description adds no additional parameter semantics beyond implying historical data retrieval, which is already covered by the tool's name and purpose. Thus, it meets the baseline for high schema coverage without compensating further.

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: 'Access historical employee count data for a company based on specific reporting periods.' It specifies the verb ('access'), resource ('historical employee count data'), and scope ('for a company based on specific reporting periods'). However, it does not explicitly differentiate from sibling tools like 'getEmployeeCount' (which might be current rather than historical), leaving room for improvement.

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 no guidance on when to use this tool versus alternatives. It mentions analyzing 'growth trends and operational changes' but does not specify prerequisites, exclusions, or direct comparisons to sibling tools such as 'getEmployeeCount' or other data retrieval tools in the list. This lack of contextual usage instructions 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.