US Government Open Data MCP

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

The description adds valuable behavioral context beyond the annotations. While annotations provide readOnlyHint=true and a title, the description discloses that this is 'Annual data only, starting from 1997' and includes a 'BEA caution: quality of these detailed estimates is lower than published aggregates.' These are important constraints and caveats that the agent needs to know, which annotations alone don't cover.

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 well-structured. It uses four sentences that each serve a distinct purpose: stating the tool's purpose and differentiation, specifying temporal constraints, providing a data quality caution, and giving usage guidance. There is no wasted verbiage, and key information is front-loaded.

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 (economic data with quality caveats) and the presence of annotations (readOnlyHint) but no output schema, the description does a good job of providing necessary context. It covers purpose, differentiation, temporal scope, data quality warnings, and prerequisite usage. The main gap is the lack of output format details, but this is partially mitigated by the tool's read-only nature and clear parameter guidance.

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 fully documents the three parameters (table_id, year, industry). The description adds minimal parameter semantics beyond the schema—it mentions using 'bea_dataset_info' to discover valid TableIDs and Industry codes, which provides context for two parameters. However, this is marginal value given the comprehensive schema descriptions.

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 Underlying GDP by Industry' with the specific clarification that it provides 'more industry detail than the main GDPbyIndustry dataset.' This distinguishes it from its sibling 'bea_gdp_by_industry' and other BEA tools, making the verb+resource+differentiation explicit.

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 versus alternatives. It states: 'Use bea_dataset_info to discover valid TableIDs and Industry codes,' naming a specific sibling tool for prerequisite discovery. It also distinguishes this tool from 'bea_gdp_by_industry' by noting it offers 'more industry detail,' helping the agent choose appropriately.

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