US Government Open Data MCP

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

Annotations indicate readOnlyHint=true, and the description aligns with this by describing a data retrieval operation ('Get Fixed Assets data'). It adds valuable context beyond annotations: the update frequency ('updated once per year'), data scope ('Annual data only'), and timing ('late August – early October'), which are not covered by annotations. No contradiction with annotations exists.

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 well-structured and front-loaded, starting with the core purpose, followed by data coverage, update details, common tables, and a clear directive for discovery. Each sentence adds essential information without redundancy, making it efficient and easy to parse.

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 (2 parameters, no output schema), the description is largely complete. It covers purpose, data scope, update frequency, common tables, and references a sibling tool for discovery. However, it lacks details on output format or error handling, which could be useful given the absence of an output schema. Annotations provide read-only assurance, but more behavioral context (e.g., response structure) would enhance completeness.

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%, providing detailed descriptions for both parameters (table_name and year). The description adds minimal semantic value beyond the schema: it mentions common table names like FAAt101 and FAAt201, which slightly enriches understanding but doesn't explain parameter usage or constraints beyond what's in the schema. This meets the baseline of 3 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 tool's purpose with specific verbs ('Get Fixed Assets data') and resources ('net stock, depreciation, and investment tables'), covering private/government fixed assets, equipment, structures, and IP products. It distinguishes from sibling tools by specifying it's for Fixed Assets data, unlike other BEA tools like bea_gdp_by_industry or bea_personal_income.

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 vs alternatives: it states 'Annual data only, updated once per year (late August – early October)' and 'Use bea_dataset_info to discover all table names,' directly naming the sibling tool for discovery. It also clarifies that common tables include FAAt101, FAAt201, FAAt401, and FAAt801, helping users understand typical use cases.

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