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 describes the output ('percentage of students at each NAEP achievement level') and emphasizes its importance ('KEY LITERACY/NUMERACY METRIC'), but does not cover critical behaviors such as data format, potential limitations (e.g., availability by year/subject), or error handling. This is a significant gap for a data retrieval tool with no 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: the first sentence states the purpose clearly, followed by context and an example. It avoids unnecessary repetition, though the second sentence could be slightly more concise. Overall, it is efficient with minimal waste.

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 (5 parameters, no output schema, no annotations), the description provides a clear purpose and context but lacks completeness. It does not explain the return format (e.g., structured data or raw percentages), handling of missing data, or how to interpret results beyond the example. This is adequate for basic understanding but leaves gaps for effective tool invocation.

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 (subject, grade, variable, jurisdiction, year) with descriptions. The description does not add any parameter-specific details beyond what the schema provides, such as explaining the 'variable' options further or clarifying default behaviors. 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: 'Get the percentage of students at each NAEP achievement level: Below Basic, Basic, Proficient, Advanced.' It specifies the verb ('Get'), resource ('percentage of students'), and scope ('NAEP achievement levels'), and distinguishes itself from siblings by focusing on achievement levels rather than scores, gaps, or variables (e.g., naep_scores, naep_compare_groups).

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 ('37% of 4th graders scored Below Basic in reading comes from this data'), suggesting it's for retrieving achievement-level percentages. However, it lacks explicit guidance on when to use this tool versus alternatives like naep_scores (for average scores) or naep_compare_groups (for comparisons), 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.