US Government Open Data MCP

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

Annotations provide readOnlyHint=true, indicating this is a safe read operation. The description adds context about the data content ('interrelationships between U.S. producers and users') and the need to discover table IDs first, which is useful beyond the annotations. However, it doesn't disclose other behavioral traits like rate limits, error handling, or response format, leaving some gaps in transparency.

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. The first sentence states the purpose, the second adds context, and the third provides critical usage guidance. Every sentence earns its place with no wasted words, and the 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 moderate complexity (2 required parameters, no output schema), the description is reasonably complete. It covers purpose, data context, and parameter discovery guidance. However, it lacks information about the return format (e.g., structure of the statistics) and any limitations (e.g., date ranges for available years), which would be helpful for an agent to understand what to expect.

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%, with clear descriptions for both parameters (table_id and year). The description reinforces the table_id parameter by explaining how to discover available values using bea_dataset_info, adding semantic context. However, it doesn't provide additional details beyond what the schema already covers, such as format examples for the year parameter or constraints on table_id values.

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 Input-Output statistics — Make Tables, Use Tables, and Requirements tables. Shows interrelationships between U.S. producers and users.' It specifies the verb ('Get'), resource ('Input-Output statistics'), and scope ('U.S. producers and users'). However, it doesn't explicitly differentiate from sibling tools like bea_gdp_by_industry or bea_nipa_underlying_detail, which are also BEA economic data tools, though the focus on input-output tables is distinct.

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: 'Use bea_dataset_info (action='get_values', dataset_name='InputOutput', parameter_name='TableID') to discover available table IDs.' This tells the agent exactly how to find the necessary parameter values before invoking the tool, addressing a key prerequisite. It doesn't mention alternatives, but the guidance is clear and actionable.

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