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 tool uses an API for DCF analysis with 'detailed inputs' and offers 'personalized and precise valuation,' but fails to disclose critical behavioral traits: whether it's a read-only or mutating operation, authentication requirements, rate limits, error handling, or what the output looks like (e.g., valuation results). For a complex financial tool with no annotation coverage, 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 two sentences and relatively concise, but it could be more front-loaded. The first sentence states the purpose clearly, but the second adds vague marketing language ('more personalized and precise') without substantive detail. While not verbose, it doesn't maximize information density for a complex tool, and some phrases like 'fine-tune their assumptions' could be more specific.

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 (19 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral aspects (e.g., safety, performance), parameter semantics, output format, and usage guidelines. Without annotations or output schema, the description should provide more context to guide effective use, but it falls short, making it inadequate for such a sophisticated financial analysis tool.

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 19 parameters with 0% description coverage in the schema itself, meaning no parameter details are provided in structured fields. The description adds no information about parameters beyond implying 'detailed inputs' and 'assumptions and variables,' without explaining what each parameter means, their units, ranges, or how they interact. Given the low schema coverage and high parameter count, the description fails to compensate, leaving parameters largely undocumented.

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: 'Run a tailored Discounted Cash Flow (DCF) analysis using the FMP Custom DCF Advanced API.' It specifies the verb ('run'), resource ('DCF analysis'), and method ('FMP Custom DCF Advanced API'), making the action clear. However, it doesn't explicitly differentiate from its sibling 'calculateCustomLeveredDCF', which appears to be a similar valuation tool, so it doesn't reach the highest score.

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 guidance: it mentions 'detailed inputs' and 'fine-tune assumptions' for 'more personalized and precise valuation,' which implies usage when custom parameters are needed. However, it lacks explicit when-to-use criteria, prerequisites, or alternatives (e.g., when to choose this over 'calculateCustomLeveredDCF' or other valuation tools like 'getDCFValuation'). No exclusions or clear context are stated, leaving usage vague.

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