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 the tool's behavior: it's a search operation (implied read-only), returns product data with specific fields (active ingredients, packaging info, openfda annotations), and includes example queries that demonstrate query syntax and filtering capabilities. However, it doesn't explicitly mention rate limits, authentication needs, or pagination details beyond the 'limit' parameter, leaving some behavioral aspects uncovered.

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: it starts with the core purpose, then lists searchable fields, provides practical examples, and ends with count fields for aggregation. Every sentence adds value—there's no redundant or vague information. It efficiently conveys necessary details in a compact format, making it easy for an AI agent to parse and apply.

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 query syntax), no annotations, and no output schema, the description does a good job of covering key aspects: purpose, usage examples, and data fields. It provides enough context for an agent to use the tool effectively, including query construction and result interpretation. However, it lacks details on output structure (e.g., response format, error handling) and doesn't mention potential limitations like API rate limits or authentication, which could be important for completeness.

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 input schema has 100% description coverage, so the baseline is 3. The description adds significant value beyond the schema by providing concrete examples of search queries (e.g., 'brand_name:"Tylenol"') and listing searchable fields (brand name, generic name, etc.), which helps users understand how to construct the 'search' parameter effectively. It also mentions 'count fields' for aggregation, which isn't covered in the schema. However, it doesn't fully explain all possible query operators or the 'limit' parameter's default behavior beyond the schema.

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 the NDC Directory — National Drug Code product listings (132K+ records).' It specifies the exact resource (NDC Directory with 132K+ records) and the action (search), distinguishing it from sibling tools which are unrelated to FDA drug data. The description goes beyond the name by explaining the scope and scale of the dataset.

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 explicit usage guidance with multiple concrete examples (e.g., 'brand_name:"Tylenol"', 'dea_schedule:"CII"'), showing exactly how to formulate queries for common use cases. It also lists specific searchable fields (brand name, generic name, dosage form, etc.) and mentions count fields for aggregation, giving clear context for when to use this tool. No alternatives are needed since sibling tools are unrelated.

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