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 data is returned (incidents broken down by categories) and mentions an optional filter, but does not disclose critical behavioral traits such as data freshness, rate limits, authentication needs, error handling, or whether it's a read-only operation. For a data retrieval tool with no annotations, this leaves significant gaps in understanding its behavior.

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 two sentences: the first states the core purpose and return data, and the second adds the optional filter with examples. Every sentence adds value without redundancy, making it front-loaded and appropriately sized for the tool's complexity.

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 complexity (6 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and data breakdowns well, but lacks details on behavioral aspects (e.g., rate limits, errors) and output format. Without an output schema, the description should ideally hint at the return structure, but it only lists categories without specifying format (e.g., JSON, pagination).

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 (e.g., state abbreviations, bias codes with enums, data types, year ranges). The description adds minimal value beyond the schema by mentioning the bias code filter with examples (e.g., '12'=Anti-Black), but does not provide additional semantics for other parameters. Baseline 3 is appropriate when 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 a specific verb ('Get') and resource ('hate crime data from the FBI'), specifying the granularity levels (national, state, or agency) and the breakdown dimensions (bias category, offense type, etc.). It distinguishes itself from sibling tools like 'fbi_arrest_data' or 'fbi_crime_summarized' by focusing exclusively on hate crime data with detailed breakdowns.

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 by mentioning the data levels (national, state, agency) and an optional filter (bias code), but does not explicitly state when to use this tool versus alternatives like 'fbi_crime_summarized' or 'fbi_nibrs'. It provides some context (e.g., filtering by bias code) but lacks clear exclusions or comparisons to sibling tools.

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