Gain access to average executive compensation data across various industries with the FMP Executive Compensation Benchmark API. This API provides essential insights for comparing executive pay by industry, helping you understand compensation trends and benchmarks.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Year to get benchmark data for |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry full behavioral disclosure. The description only mentions 'average' and 'benchmark' but does not disclose response structure, optional year behavior, data coverage limits, or any other operational details. It is more promotional than informative.
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 at two sentences, but the second sentence largely restates the first ('comparing executive pay by industry' vs 'average executive compensation data across industries'). It is not bloated, but some redundancy prevents a 5.
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 simplicity (one optional parameter, no output schema), the description gives the core idea but omits practical details like what the response looks like and how the optional 'year' parameter behaves. It is minimally adequate but not comprehensive.
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% coverage with a clear description for the 'year' parameter, so the baseline is 3. The tool description adds no additional parameter-level meaning beyond what the schema already provides.
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 identifies the tool as providing average executive compensation data across various industries, which is a specific resource and distinguishes it from related tools like getExecutiveCompensation (company-specific) and getCompanyExecutives. The verb 'Gain access' is slightly generic, but the resource scope is precise.
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 for comparing executive pay by industry and understanding compensation benchmarks, but it does not explicitly state when to prefer this tool over alternatives like getExecutiveCompensation or getCompanyExecutives. No exclusions or alternative references are provided.
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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