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. It describes what the tool returns (FMR data by bedroom count) and its application (Section 8 vouchers), but lacks details on behavioral traits such as rate limits, authentication needs, error handling, or data freshness. The description adds some context but does not fully compensate for the absence of annotations.

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 with the core purpose in the first sentence, followed by supporting details in a second sentence. Every sentence earns its place by adding essential information (output format and application) without redundancy or fluff, making it highly efficient and well-structured.

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 (3 parameters, no output schema, no annotations), the description is fairly complete: it covers purpose, scope, output format, and application. However, it lacks details on return structure (e.g., data format, units) and error cases, which would be helpful for an agent to use it correctly without an output schema.

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 all parameters thoroughly. The description adds marginal value by clarifying the geographic scope (county, metro, state) and referencing hud_list_counties for entity IDs, but does not provide additional syntax, format, or usage details beyond what the schema specifies. This meets the baseline for high schema coverage.

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 specific action ('Get'), resource ('HUD Fair Market Rents'), and scope ('for a county, metro area, or entire state'), distinguishing it from sibling tools like hud_list_counties or hud_income_limits. It also specifies the output format ('monthly rent by bedroom count') and purpose ('determines Section 8 voucher amounts'), making the purpose highly specific and well-defined.

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 on when to use this tool (to get FMR data for geographic areas) and hints at alternatives by referencing hud_list_counties for obtaining entity IDs. However, it does not explicitly state when not to use it or name direct alternatives for similar data (e.g., hud_income_limits), 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.