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. It discloses the tool's read-only nature implicitly through 'Get' and 'Returns', and provides useful behavioral context like NPL status codes and cross-referencing suggestions. However, it doesn't mention rate limits, authentication needs, pagination behavior, or error conditions that would be important for a production API tool.

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 efficiently structured with three focused sentences: purpose statement, output details, and usage context. Every sentence adds value - the first establishes core functionality, the second documents output semantics, and the third provides integration guidance. No wasted words or redundant information.

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 tool with 2 parameters, 100% schema coverage, and no output schema, the description provides good contextual completeness. It explains what the tool returns, documents status codes, and suggests integration patterns. The main gap is lack of output format details (JSON structure, pagination, error formats) which would be helpful given no output schema exists.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the NPL status codes ('F', 'P', 'D', 'N') which are referenced in the output but not documented in the input schema. It also mentions cross-referencing with other datasets, providing context for how the results might be used beyond the basic parameter definitions.

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 specific action ('Get Superfund (CERCLA) contaminated sites') and resource ('by state'), with explicit output fields listed. It distinguishes from sibling tools by focusing on EPA Superfund data, unlike the many BEA, CDC, Census, and other agency tools in the list.

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: to retrieve Superfund site data for a specific state. It mentions cross-referencing with other datasets, implying integration use cases. However, it doesn't explicitly state when NOT to use it or name specific alternative tools for similar data.

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