US Government Open Data MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds context by specifying the data source (BRFSS survey) and listing disability types, which helps set expectations. However, it doesn't disclose behavioral traits like rate limits, error handling, or data freshness beyond what annotations cover.

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 two sentences: one stating the purpose and source, another listing types. It's front-loaded with key information and avoids redundancy, though the type list is lengthy but necessary for clarity. Every sentence earns its place by adding specific details.

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), the description is adequate but not complete. It covers the data source and types, but lacks information on output format, pagination (implied by limit parameter), or error conditions. Annotations help with safety, but more context could improve usability.

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 parameters are well-documented in the schema. The description adds minimal value by listing disability types (which partially overlaps with the schema's enum-like description for disability_type) but doesn't provide additional semantics like examples or edge cases 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 verb ('Get') and resource ('disability prevalence by state and type from BRFSS survey'), making the purpose specific. It distinguishes from siblings by focusing on CDC disability data, unlike other CDC tools (e.g., cdc_covid, cdc_life_expectancy) or non-CDC tools in the list.

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 implies usage for retrieving disability prevalence data, but provides no explicit guidance on when to use this tool versus alternatives (e.g., other CDC tools like cdc_query or cdc_places_health). It lists disability types, which hints at scope, but lacks when-not-to-use or prerequisite information.

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