US Government Open Data MCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds context beyond basic functionality: it explains the classification system (Class I to III), notes data limitations ('Records before June 2012 may lack some fields'), and provides example queries and count fields, giving practical insights into how the tool behaves and what to expect.

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 well-structured and front-loaded with the core purpose, followed by classification details, data caveats, example searches, and count fields. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 complexity of a search tool with no output schema and no annotations, the description does a good job of being complete. It covers purpose, usage examples, data limitations, and result interpretation (count fields). However, it could improve by explicitly mentioning the tool's output format or any rate limits, though the examples and count fields partially compensate.

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% description coverage, so the baseline is 3. The description adds value by providing example searches that illustrate how to use the 'search' parameter with specific syntax (e.g., 'classification:"Class I"', date ranges, exact phrases), and mentions 'count fields' like voluntary_mandated.exact, which helps interpret results, enhancing understanding 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's purpose: 'Search FDA device recall enforcement reports.' It specifies the resource (FDA device recall enforcement reports) and the action (search), and distinguishes it from siblings by focusing on device recalls, unlike other FDA tools like fda_drug_recalls or fda_food_recalls.

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 implied usage through example searches (e.g., for Class I recalls, date ranges, or companies), but does not explicitly state when to use this tool versus alternatives like fda_device_recalls or other FDA tools. It mentions the classification system and data limitations, offering some contextual guidance.

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