EPA Air Quality System (AQS) MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the aggregation method (quarterly summaries) and typical use cases, but doesn't mention authentication requirements (though the schema covers this), rate limits, error conditions, or what the output format looks like. For a data retrieval tool with no annotations, this provides basic behavioral context but lacks operational details.

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 perfectly structured with three sentences that each earn their place: first states the core functionality, second explains the aggregation method and statistics, third provides usage context. It's front-loaded with the main purpose and contains zero 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?

Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description provides adequate context for understanding what the tool does and when to use it. However, it doesn't explain what the return data looks like (structure, format, units) or address potential limitations (data availability, processing time). For a data retrieval tool without output schema, more detail about the response would be helpful.

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?

With 100% schema description coverage, the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions aggregation by calendar quarter, which relates to the date parameters, but doesn't provide additional syntax or format details. The baseline of 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 with specific verbs ('retrieve quarterly summary data') and resources ('air quality monitoring sites in a state'), distinguishing it from siblings by specifying quarterly aggregation and statewide scope. It explicitly mentions aggregation by calendar quarter and the types of statistics provided (observation counts, arithmetic means, maximum values).

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 statewide air quality analysis and comparing trends across different regions'), which helps differentiate it from county/site-level quarterly tools. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools (e.g., annual_summary_by_state for yearly data).

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