US Government Open Data MCP

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

With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions 'significance testing' and 'statistically significant', it doesn't describe what the tool actually returns (tables, charts, p-values), how results are formatted, whether there are rate limits, or what happens with invalid parameter combinations. For a statistical analysis tool with no annotation coverage, this leaves significant behavioral gaps.

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?

Three concise sentences with zero waste. First sentence states core functionality, second adds key feature (significance testing), third provides practical use case. Every sentence earns its place, and information is front-loaded appropriately.

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?

For a statistical comparison tool with 5 parameters, 100% schema coverage, but no output schema and no annotations, the description is minimally adequate. It explains the 'what' and 'why' but lacks crucial information about return format, error handling, and practical constraints. The COVID example helps but doesn't compensate for missing behavioral context.

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 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions 'years' implicitly through 'assessment years' but provides no additional syntax or format guidance. Baseline 3 is appropriate when 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 with specific verbs ('Compare NAEP scores', 'Shows whether score changes...are statistically significant') and identifies the resource ('NAEP scores across assessment years'). It distinguishes from sibling tools like 'naep_scores' by focusing specifically on year-over-year comparison with significance testing.

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 ('Great for tracking the COVID learning loss and recovery'), which implies it's for analyzing educational trends over time. However, it doesn't explicitly state when NOT to use it or name specific alternative tools among the many NAEP siblings for different comparison scenarios.

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