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 'detailed inputs' and 'personalized and precise valuation,' but fails to disclose critical traits such as whether this is a read-only or write operation, potential rate limits, authentication needs, error handling, or what the output looks like (especially since there's no output schema). This leaves significant gaps for an AI agent to understand the tool's 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 two sentences long and front-loaded with the core purpose. It avoids unnecessary fluff, but could be slightly more structured by explicitly listing key assumptions or output format. Overall, it's efficient and earns its place without waste.

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 (19 nested parameters, no annotations, no output schema), the description is insufficient. It doesn't explain the return values, error conditions, or how the 'tailored' analysis differs from standard DCF tools. For a tool with rich inputs and no structured output, more context is needed to guide proper usage.

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 1 parameter (an object with 19 nested properties), and schema description coverage is 0%, meaning none of the nested properties have descriptions in the schema. The description adds minimal value by hinting at 'detailed inputs' and 'assumptions,' but doesn't explain what the parameters mean beyond what's implied by their names. With low coverage, the description doesn't adequately compensate, so it meets the baseline for moderate schema support.

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 context ('FMP Custom DCF Advanced API'), making it easy to understand. However, it doesn't explicitly differentiate from its sibling 'calculateCustomDCF' (which appears to be a similar tool), missing full distinction.

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 implies usage by mentioning 'detailed inputs' and 'fine-tune assumptions,' suggesting it's for personalized valuation, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'calculateCustomDCF' or other valuation tools in the sibling list. No when-not-to-use or prerequisite information is included.

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