US Government Open Data MCP

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

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds value by specifying supported datasets and common variables, which helps set expectations. However, it does not disclose additional behavioral traits like rate limits, authentication needs, or pagination behavior, leaving gaps despite the annotation coverage.

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, starting with the core purpose. It uses two sentences efficiently, with the second sentence providing helpful examples without redundancy. However, it could be slightly more structured by separating usage tips into bullet points for clarity.

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 (4 parameters, no output schema) and rich schema coverage, the description is adequate but incomplete. It covers the purpose and examples but lacks details on error handling, response format, or limitations. With annotations covering safety, it meets minimum viability but leaves room for improvement in contextual depth.

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 documents all parameters thoroughly. The description adds minimal value by listing common examples for datasets and variables, but does not provide additional semantics beyond what the schema specifies (e.g., format details or constraints). Baseline 3 is appropriate as the schema does the heavy lifting.

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: 'Query the U.S. Census Bureau Data API' with specific resources mentioned ('ACS, Decennial Census, Population Estimates, Economic Census, and more') and the action ('Returns data for specified variables and geography'). It distinguishes itself from sibling tools (like 'census_population' or 'census_search_variables') by being a general query tool rather than a specialized one.

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 implied usage context by listing common datasets and variables, suggesting when to use this tool for Census data queries. However, it lacks explicit guidance on when to choose this over alternatives (e.g., 'census_population' for population-specific data or 'census_search_variables' for variable discovery), and does not mention prerequisites or exclusions.

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