US Government Open Data MCP

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 effectively describes key traits: it's a search operation (implying read-only), includes example queries showing syntax and capabilities, mentions count fields for aggregation, and implies a default limit of 10 with a max of 100 (though this is also in the schema). It lacks details on rate limits, authentication needs, or pagination behavior, but covers essential usage context well.

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 appropriately sized and front-loaded: it starts with a clear purpose statement, followed by example searches that earn their place by illustrating usage, and ends with a note on count fields. Every sentence adds practical value without redundancy, making it efficient and well-structured 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 the complexity of a search tool with 2 parameters, 100% schema coverage, no annotations, and no output schema, the description is largely complete. It explains what the tool does, provides usage examples, and hints at result interpretation (count fields). However, it does not detail the output format or potential errors, leaving some gaps in full contextual understanding.

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 thoroughly. The description adds significant value beyond the schema by providing concrete example queries (e.g., 'openfda.brand_name:"Tylenol"') and mentioning count fields, which clarify how to construct effective searches and interpret results, compensating for the schema's technical descriptions.

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 a specific verb ('Search') and resource ('FDA drug product labeling'), and distinguishes it from siblings by specifying the exact dataset (package inserts/prescribing information - SPL) and listing content types like indications, warnings, and adverse reactions. This is distinct from other FDA tools like fda_approved_drugs or fda_drug_recalls.

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 usage through multiple example searches (e.g., searching by brand name, boxed warnings, date range, product type), which implicitly guides when to use this tool. However, it does not explicitly state when not to use it or name alternatives among siblings, such as fda_drug_counts for aggregated data instead of detailed labeling.

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