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 the data source (Drugs@FDA database) and types of information returned (approval history, active ingredients, marketing status), but lacks details on behavioral traits such as rate limits, authentication needs, error handling, or pagination. The examples hint at query syntax but do not fully explain behavioral constraints.

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 front-loaded: the first sentence states the core purpose, followed by search capabilities and output details, then practical examples. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 no annotations and no output schema, the description provides a good overview of purpose and usage but lacks completeness in behavioral context (e.g., no info on response format, error cases, or system constraints). For a search tool with 2 parameters and rich query potential, more details on output structure or limitations would be beneficial.

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 both parameters (search query syntax and limit with defaults). The description adds value by providing concrete search examples (e.g., 'openfda.brand_name:"Ozempic"') that illustrate parameter usage beyond the schema's generic examples, enhancing understanding of how to construct effective queries.

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 FDA-approved drugs (Drugs@FDA database)' with specific search capabilities ('by brand name, sponsor/manufacturer, or application number') and output details ('approval history, active ingredients, and marketing status'). It distinguishes itself from sibling tools by focusing on FDA drug approvals, unlike the many other data tools in the list (e.g., BEA, BLS, CDC 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 implies usage through examples (e.g., searching for Ozempic, Pfizer approvals, or by ingredient), but does not explicitly state when to use this tool versus alternatives. There is no mention of prerequisites, limitations, or comparisons with other FDA-related tools in the sibling list (e.g., fda_drug_counts, fda_drug_labels).

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