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 of behavioral disclosure. It mentions search functionality but lacks critical details: it does not specify if this is a read-only operation, what the response format looks like, whether there are rate limits, authentication requirements, or error handling. For a search tool with zero annotation coverage, 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 highly concise and well-structured: a clear purpose statement followed by two example searches. Every sentence earns its place by providing essential information without redundancy, 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 moderate complexity (search with two parameters) and lack of annotations and output schema, the description is partially complete. It covers the purpose and basic usage with examples but misses behavioral aspects like response format, error handling, and operational constraints. This leaves gaps for an AI agent to invoke the tool effectively without additional context.

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 schema already documents both parameters ('search' and 'limit') thoroughly. The description adds value by providing concrete search examples (e.g., 'unii:"L7V4I673D2"') that illustrate parameter usage beyond the schema's generic examples, but it does not explain parameter interactions or default behaviors beyond what the schema states.

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 UNII (Unique Ingredient Identifiers) — links ingredient names to unique chemical IDs.' It specifies the verb ('search'), resource ('UNII'), and function ('links ingredient names to unique chemical IDs'), which distinguishes it from sibling tools focused on other FDA datasets like drugs, devices, or food events.

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 with two example searches (by UNII code and by substance name), indicating when to use specific query formats. However, it does not explicitly state when not to use this tool or name alternatives among siblings (e.g., fda_substance or fda_drug_labels), which prevents a perfect score.

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