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 mentions required parameters and optional filters but lacks behavioral details such as rate limits, authentication needs, pagination behavior (implied by 'limit' parameter but not explained), error handling, or what the output looks like (no output schema). For a data retrieval tool with 7 parameters, this is a significant gap.

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 front-loaded with the core purpose in the first sentence, followed by requirements and optional filters in two concise sentences. Every sentence earns its place with no wasted words, making it easy to scan and understand 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 complexity (7 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and parameters but lacks crucial context: no information on output format, error conditions, rate limits, or how results are structured. For a data retrieval tool with multiple filters, this leaves significant gaps for an AI agent.

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 thoroughly (e.g., dataset_id enum values, date formats, station/location examples, datatype_id options, limit default). The description adds minimal value beyond the schema by listing data types and mentioning optional filters, but doesn't provide additional syntax or usage details. Baseline 3 is appropriate when 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 climate observations (temperature, precipitation, snow, wind) from NOAA.' It specifies the verb ('Get'), resource ('climate observations'), and data types. However, it doesn't explicitly differentiate from its sibling tools (e.g., noaa_datasets, noaa_stations, noaa_locations), which are listed but not distinguished in the description.

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 some usage context: 'Requires dataset ID + date range. Optionally filter by station or location.' This implies when to use it (for climate data with date ranges) and mentions optional filters. However, it doesn't explicitly state when not to use it or name alternatives (e.g., noaa_datasets for metadata), leaving guidance incomplete.

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