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 valuable behavioral context beyond annotations by warning about lower data quality compared to published aggregates and listing common table examples, which helps set expectations about output reliability and typical use cases.

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 with three sentences: a clear purpose statement, a quality caution, and usage guidance with examples. Each sentence adds distinct value without redundancy, making it easy to parse and understand 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 (3 parameters, no output schema), the description provides good contextual completeness. It covers purpose, quality caveats, usage guidance, and common examples. The main gap is the lack of output format details, but the description compensates well with practical guidance for a data retrieval tool.

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 three parameters. The description adds minimal parameter semantics by mentioning a default table ('U20305') and referencing 'bea_dataset_info' for table discovery, but doesn't provide additional meaning beyond what the schema already specifies for parameters like frequency or year.

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 specific action ('Get') and resource ('NIPA underlying detail data'), with additional clarification about granular breakdowns. It explicitly distinguishes this tool from its sibling 'bea_dataset_info' by mentioning it as a discovery mechanism, establishing clear functional boundaries.

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 explicit guidance on when to use this tool (for granular national account breakdowns) and when to use an alternative ('Use bea_dataset_info to discover all tables'). It also includes a caution about data quality, which helps inform appropriate usage contexts.

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