US Government Open Data MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations include 'readOnlyHint: true', which the description does not contradict, as 'Get' implies a read operation. The description adds value by specifying the aggregation fields and use cases, but it does not disclose additional behavioral traits like rate limits, pagination, or data freshness. With annotations covering the read-only aspect, the description provides some context but lacks depth in other behavioral details.

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 front-loaded with the core purpose in the first sentence, followed by usage examples and a clear list of aggregation fields. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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 (7 parameters, 1 required), 100% schema coverage, and no output schema, the description is mostly complete. It covers the purpose, usage, and parameters well, but it could benefit from mentioning the output format or any limitations, such as date range constraints or result size, to fully compensate for the lack of output schema.

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 schema description coverage is 100%, so the schema already documents all parameters. The description adds meaning by listing the aggregation fields and providing examples of use cases, which helps clarify the 'field' parameter's purpose. However, it does not add syntax or format details beyond what the schema provides, keeping it at a baseline with slight enhancement.

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 with a specific verb ('Get') and resource ('complaint counts grouped by a field'), and it distinguishes itself from siblings by specifying the aggregation fields. It explicitly lists the grouping options, making the purpose unambiguous and distinct from other tools.

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 clear context on when to use the tool ('Useful for ranking companies by complaint volume, identifying top issues, or comparing states'), which helps guide its application. However, it does not explicitly mention when not to use it or name specific alternatives among siblings, such as 'cfpb_complaint_detail' or 'cfpb_search_complaints', leaving some room for improvement in differentiation.

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