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. It discloses that the tool returns data (a read operation) and describes the content (public water systems with population served, source type, system type). However, it lacks details on behavioral traits like rate limits, error handling, or pagination (only hints at a default row limit). This is adequate but has gaps for a tool with no annotations.

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 core purpose, followed by details on returns and system types, ending with a usage suggestion. Every sentence adds value without redundancy, making it efficient and well-structured.

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 moderate complexity (2 parameters, no output schema, no annotations), the description is fairly complete. It covers purpose, returns, parameter context, and usage suggestions. However, without an output schema, it could benefit from more detail on the response format (e.g., structure of returned data), though the system type explanations partially compensate.

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 fully documents the two parameters. The description adds value by explaining the data returned (e.g., system types with definitions like 'CWS', 'NTNCWS', 'TNCWS') and suggesting cross-referencing for analysis, which provides context beyond the schema's 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 tool's purpose: 'Get Safe Drinking Water Information System (SDWIS) data by state.' It specifies the verb ('Get'), resource ('SDWIS data'), and scope ('by state'), and distinguishes itself from sibling tools by focusing on EPA drinking water data, unlike the many BEA, BLS, CDC, etc., tools listed.

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: for retrieving public water system data by state. It suggests cross-referencing with CDC health data and Census population for per-capita analysis, implying integration with other tools. However, it does not explicitly state when not to use it or name specific alternatives among siblings.

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