US Government Open Data MCP

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

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds valuable behavioral context beyond annotations: it mentions auto-retry with fuzzy search for company names, lists specific product categories, and notes the database size (13M+ records). It does not cover rate limits or authentication needs, but these are not critical omissions given the read-only nature.

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 efficiently structured: it starts with the core purpose, lists searchable fields, explains key behaviors (fuzzy search), and provides product examples—all in four concise sentences. Every sentence adds value without redundancy, making it easy to scan and understand.

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 (14 parameters, no output schema), the description is mostly complete. It covers the purpose, usage context, and key behaviors. However, it lacks details on output format (e.g., pagination, field structure) and does not mention default values or error handling, which would be helpful for a search tool with many parameters.

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 14 parameters. The description adds some semantic context by listing product examples and explaining fuzzy search for company names, but it does not provide significant additional meaning beyond what the schema already specifies. This meets the baseline for high schema coverage.

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 searches the CFPB consumer complaint database (13M+ records) and returns individual complaints with company responses. It specifies the searchable fields (company, product, state, issue, date, keyword) and distinguishes itself from sibling tools like cfpb_complaint_aggregations or cfpb_complaint_trends by focusing on detailed search results rather than summaries or trends.

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 (to find complaints by various filters) and implies alternatives by mentioning specific search capabilities. However, it does not explicitly state when not to use it or name alternative tools for different use cases, such as cfpb_complaint_aggregations for summary data.

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