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 the dataset size (~1.3K reports) and composition (e-cigarettes dominate), which adds useful context. However, it lacks details on permissions, rate limits, pagination, or error handling. The description does not contradict any annotations, but it could provide more behavioral insights for a search 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 well-structured and concise, with no wasted words. It starts with the core purpose, provides dataset context, gives practical examples, and ends with helpful count field information. Every sentence adds value, 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 (search with two parameters), no annotations, and no output schema, the description does a good job of covering key aspects: purpose, dataset context, usage examples, and result interpretation hints. However, it could improve by mentioning response format or limitations, such as the max limit of 100 results, which is only in the schema.

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 schema description coverage is 100%, so the baseline is 3. The description adds value by providing concrete search examples that illustrate how to use the 'search' parameter effectively, and it mentions 'Count fields: tobacco_products.exact, reported_health_problems.exact,' which helps interpret results. This goes beyond the schema's generic query description, justifying a higher score.

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: 'Search tobacco product problem reports (~1.3K reports).' It specifies the resource (tobacco product problem reports), the action (search), and provides helpful context about the dataset size and composition ('E-cigarettes/vaping products dominate (~60% of reports)'). This distinguishes it from sibling tools that handle different datasets like BEA, BLS, or other FDA tools.

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 usage context with example searches ('date_submitted:[20180101+TO+20200723]' and 'nonuser_affected:"Yes"'), which helps users understand when to apply specific query patterns. However, it does not explicitly state when to use this tool versus alternatives or mention any prerequisites, keeping it from a perfect score.

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