US Government Open Data MCP

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return values ('innerdiff1', 'innerdiff2', and the gap between them'), which adds some context about output structure. However, it does not describe error conditions, rate limits, authentication needs, data freshness, or what 'achievement gaps' are calculated from (e.g., score differences). For a statistical analysis tool with no annotations, 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?

The description is highly concise and well-structured: one sentence states the purpose, one gives an example, and one explains the return values. Every sentence earns its place by adding clarity without redundancy. It is front-loaded with the core functionality.

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 complexity (statistical gap analysis over time), no annotations, no output schema, and rich schema coverage, the description is partially complete. It explains the purpose and output structure but lacks details on behavioral aspects like calculation methodology, error handling, or data sources. For a tool with 5 parameters and no annotations, it should do more to compensate for the missing structured data.

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 (e.g., subject aliases, grade values, variable examples, years format, jurisdiction default). The description does not add any parameter-specific details beyond what the schema provides, such as explaining how 'variable' relates to 'demographic groups' or clarifying 'years' ordering. Baseline 3 is appropriate when 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: 'Compare how achievement gaps between demographic groups change over time.' It specifies the verb ('compare'), resource ('achievement gaps'), and scope ('over time'), and includes a concrete example. It distinguishes from siblings like 'naep_compare_groups' or 'naep_compare_years' by focusing specifically on gap changes over time.

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 implies usage through the example ('Is the racial achievement gap in reading getting bigger or smaller since 2017?'), which suggests this tool is for analyzing gap trends. However, it does not explicitly state when to use this tool versus alternatives like 'naep_gap_variable_jurisdiction' or 'naep_gap_year_jurisdiction', nor does it provide exclusions or prerequisites. The guidance is contextual but not explicit.

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