Retrieve detailed information on company executives with the FMP Company Executives API. This API provides essential data about key executives, including their name, title, compensation, and other demographic details such as gender and year of birth.
| Name | Required | Description | Default |
|---|---|---|---|
| active | No | Filter for active executives | |
| symbol | Yes | Stock symbol |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a read operation via the verb 'Retrieve' and provides context about the returned data fields. With no annotations provided, the description carries the transparency burden, but it does not mention potential errors, authentication requirements, or the behavior of the 'active' filter. However, for a simple read-only API, the disclosure is minimally adequate.
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, front-loaded with the action verb 'Retrieve', and includes relevant detail without redundancy. It efficiently conveys the tool's purpose and output scope.
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?
For a 2-parameter read API without an output schema, the description covers the purpose, data fields, and general context. It lacks explicit sibling differentiation and does not elaborate on the 'active' parameter, but it provides enough information for an agent to select and invoke the tool correctly.
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 already documents both parameters with descriptions: 'symbol' as 'Stock symbol' and 'active' as 'Filter for active executives', giving 100% coverage. The description adds context about returned fields (compensation, demographics) but does not clarify expected values for 'active' (e.g., 'true'/'false' vs. arbitrary strings), so it adds only marginal value beyond the schema.
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 retrieves detailed executive information via the FMP API and lists specific fields (name, title, compensation, demographic details). It is specific enough to identify the tool's focus, but it does not explicitly distinguish it from siblings like getExecutiveCompensation, which also deals with executive data.
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?
No explicit when-to-use or alternative guidance is provided. The description implies the tool is for retrieving executive details, but it does not mention when to prefer this tool over related ones like getExecutiveCompensation or getCompanyProfile, nor does it list any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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