US Government Open Data MCP

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

Annotations declare readOnlyHint=true, which the description aligns with by using 'Get' (a read operation). The description adds useful behavioral context beyond annotations: it explains the default TableID option, clarifies that 'Industry=ALL' returns all sectors, and mentions that not all tables support quarterly frequency. However, it doesn't disclose other behavioral traits like rate limits, authentication needs, or pagination behavior.

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: the first sentence states the core purpose, followed by specific details about TableID and industry options. Each sentence adds value, though the TableID list could be slightly condensed. There is no redundant or verbose language.

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 (4 parameters, no output schema), the description is adequate but has gaps. It covers the purpose and key parameters but lacks information on output format, error handling, or data currency. With readOnlyHint annotation, safety is covered, but more behavioral context (e.g., data latency, typical response structure) would improve completeness 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 4 parameters. The description adds minimal value beyond the schema: it lists TableID options (which are already in the schema) and clarifies the 'ALL' option for industry (also in schema). No additional syntax, format details, or examples are provided, so it meets the baseline for high schema coverage.

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 action ('Get'), the resource ('GDP contribution by industry sector nationally'), and the source ('BEA GDPbyIndustry dataset'). It specifically distinguishes this tool from siblings like 'bea_gdp_by_state' (state-level) and 'bea_gdp_national' (national GDP aggregates) by focusing on industry-sector breakdowns, providing clear differentiation.

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 clear context for when to use this tool: for national GDP data broken down by industry sector. It implicitly distinguishes from siblings by specifying the dataset scope, but does not explicitly state when NOT to use it or name specific alternatives for overlapping queries (e.g., when to choose this over 'bea_underlying_gdp_by_industry').

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