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 key behavioral traits: it's a counting operation ('count earthquakes') that returns aggregated data ('without fetching full details'), implying it's read-only and non-destructive. However, it lacks details on rate limits, authentication needs, error handling, or output format (e.g., integer count vs. structured data), which are important for a tool with 7 parameters.

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 front-loaded: the first sentence states the core purpose, and the second provides usage context with an example. Every sentence earns its place with zero waste, making it easy for an agent to parse quickly.

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 (7 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and usage but lacks behavioral details (e.g., output format, error cases) and does not reference sibling tools for context. With no output schema, the description should ideally hint at return values, which it does not.

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, with each parameter clearly documented (e.g., 'Start date: '2024-01-01''). The description adds minimal value beyond this, only implying criteria matching via 'matching criteria' and the example. Since schema coverage is high, the baseline is 3, and the description does not significantly enhance parameter understanding.

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 explicitly states the tool's purpose: 'Count earthquakes matching criteria without fetching full details.' It specifies the verb ('count'), resource ('earthquakes'), and scope ('without fetching full details'), distinguishing it from sibling tools like 'usgs_earthquakes' (which likely fetches full details). The example 'How many M5+ earthquakes occurred in 2024?' reinforces this specific purpose.

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: 'Useful for statistics.' This implies it's for aggregate counting rather than detailed retrieval. However, it does not explicitly state when not to use it or name alternatives (e.g., 'usgs_earthquakes' for full details), which prevents a score of 5.

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