US Government Open Data MCP

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds useful context beyond this by specifying the output format ('complaint counts and data for each state') and noting filter applicability, but does not disclose behavioral traits like rate limits, pagination, or data freshness. No contradiction with annotations exists.

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 highly concise and front-loaded, with three sentences that efficiently convey purpose, output utility, and filter behavior. Each sentence earns its place without redundancy, making it easy for an agent to parse quickly.

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 moderate complexity (6 optional parameters, no output schema), the description is reasonably complete. It covers purpose, output format, and filter applicability, but lacks details on response structure (e.g., JSON fields) or error handling. With annotations covering safety, it's adequate but could be more 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?

Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description adds marginal value by stating that filters 'apply the same filters as search (product, company, date, etc.)', which reinforces parameter usage but does not provide additional semantics beyond what the schema already specifies.

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 specific verbs ('Get complaint information') and resources ('broken down by state'), explicitly mentioning it returns 'complaint counts and data for each state' for 'maps and state comparisons'. It distinguishes itself from sibling tools like 'cfpb_search_complaints' by focusing on geographic aggregation rather than individual complaint retrieval.

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 for when to use this tool ('Useful for maps and state comparisons') and indicates it 'Applies the same filters as search', implicitly suggesting it's for aggregated views rather than detailed searches. However, it does not explicitly name alternatives or state when not to use it, such as for individual complaint details (handled by 'cfpb_complaint_detail').

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