US Government Open Data MCP

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns matching foods with basic nutrient info and covers a large dataset, but does not mention behavioral traits like rate limits, authentication needs, pagination details (beyond parameters), or error handling. While it adds some context (e.g., data types), it falls short of fully describing operational behavior for a search tool with 7 parameters.

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 appropriately sized and front-loaded, with the first sentence stating the core purpose, followed by key details in subsequent sentences. Every sentence adds value (e.g., coverage, data types) 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 complexity (7 parameters, no output schema, no annotations), the description is moderately complete. It covers the purpose, scope, and data types, but lacks details on output format, error cases, or behavioral constraints. Without an output schema, it should ideally describe return values more explicitly, but it does enough for basic usage while leaving gaps for advanced scenarios.

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 already documents all parameters thoroughly. The description adds value by explaining the data types ('Foundation', 'SR Legacy', etc.) in more semantic detail, which helps interpret the 'dataType' parameter beyond the enum list. However, it does not provide additional context for other parameters like 'query' or 'sortBy', so it partially compensates but not fully 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 with a specific verb ('Search') and resource ('USDA FoodData Central database for foods'), and distinguishes it from siblings by focusing on food data rather than other datasets like BEA, BLS, or census data. It also specifies the scope ('Returns matching foods with basic nutrient info') and coverage ('Covers 300K+ foods including branded products'), making it highly distinct.

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 searching foods by keyword and filtering by data type, but does not explicitly state when to use this tool versus alternatives like 'fooddata_detail' or 'fooddata_list' (which are sibling tools). It provides context on data types but lacks guidance on exclusions or prerequisites, leaving the agent to infer appropriate scenarios.

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