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 the tool 'shows' data, implying a read-only operation, but does not clarify if it requires authentication, has rate limits, pagination behavior, or error handling. For a search tool with no annotations, this leaves significant gaps in understanding how it behaves beyond basic functionality.

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 front-loaded and efficiently structured in two sentences: the first defines the tool's purpose and scope, and the second clarifies the data type and output fields. Every sentence adds value without redundancy, making it concise and easy to parse.

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?

The description adequately covers the tool's purpose and scope, but with no annotations and no output schema, it lacks details on behavioral traits (e.g., safety, performance) and return format. For a search tool with 5 parameters, this is a moderate gap, as users need more context on how results are structured and any operational constraints.

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, with each parameter well-documented (e.g., 'company' as 'Company name: 'Pfizer', 'Novo Nordisk''). The description adds no additional parameter semantics beyond the schema, such as explaining interactions between parameters or search logic. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema does the heavy lifting.

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 specific verbs ('Search') and resources ('Open Payments RESEARCH payment data'), explicitly distinguishing it from general payments. It identifies the data domain (grants, clinical research funding from pharma to doctors) and key output fields (funding amounts, sponsors, principal investigators), making it highly specific and differentiated from sibling 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 context by stating this tool is for 'research payment data' and 'separate from general payments,' which helps differentiate it from other Open Payments tools like 'open_payments_search' (likely for general payments). However, it does not explicitly name alternatives or specify when-not-to-use scenarios, such as for non-research payments or other data types.

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