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 states 'Get crop production data,' which implies a read-only operation, but does not disclose behavioral traits such as data freshness, rate limits, authentication needs, error handling, or response format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond basic functionality.

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: the first sentence states the core purpose, and the second lists commodities. Every sentence earns its place with no wasted words, making it easy to scan and understand quickly. The structure is efficient and to the point.

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 no annotations, no output schema, and a tool with 4 parameters, the description is incomplete. It lacks information on behavioral aspects (e.g., data sources, limitations), output format, or error conditions. While concise, it does not provide enough context for an agent to use the tool effectively beyond basic parameter input, especially for a data retrieval tool where response structure matters.

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 (commodity, state, year, category) with descriptions. The description adds minimal value beyond the schema by listing commodities and data categories, but does not provide additional semantics like examples, constraints, or interactions between parameters. Baseline 3 is appropriate as the schema does the heavy lifting.

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: 'Get crop production data — area planted, harvested, production, yield.' It specifies the verb ('Get') and resource ('crop production data') with concrete data categories. However, it does not explicitly differentiate from sibling tools like 'usda_livestock' or 'usda_prices', which are also USDA data tools, though the commodity list helps imply scope.

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 context by listing specific commodities (CORN, SOYBEANS, etc.), suggesting it's for those crops only. However, it does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'usda_livestock' for livestock data) or any prerequisites. The guidance is limited to the commodity scope without broader contextual advice.

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