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 approach ('Uses RCDC spending categories with an agency-based fallback'), provides practical context with common category IDs, and notes reliability considerations. However, it doesn't mention potential limitations like rate limits, error handling, or data freshness, leaving some behavioral aspects unspecified.

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 efficiently structured in three sentences: purpose statement, methodological note, and usage guidance. Each sentence adds distinct value without redundancy. It front-loads the core functionality and maintains appropriate brevity for a tool with two well-documented parameters.

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 (2 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, methodology, parameter context, and alternatives. However, without an output schema, it doesn't describe return values (e.g., format of counts/funding data), leaving a minor gap 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?

The input schema has 100% description coverage, so the baseline is 3. The description adds significant value by providing common category ID examples (e.g., '27=Cancer, 7=Alzheimer's') beyond the schema's limited list, and clarifies the tool's scope ('across fiscal years'), enhancing understanding of parameter usage in context.

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 ('Get NIH project counts and estimated funding') and resources ('for a disease/research area across fiscal years'). It distinguishes itself from sibling tools by mentioning 'nih_projects_by_agency' as an alternative for more reliable counts, showing awareness of its role in the toolset.

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 guidance on when to use this tool vs. alternatives. It states 'Uses RCDC spending categories with an agency-based fallback for more accurate counts' and directly advises 'For the most reliable counts by disease area, also try nih_projects_by_agency with the relevant institute,' clearly delineating usage contexts and alternatives.

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