Katzilla MCP

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

The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world hints, but the description specifies the data source (U.S. Department of Housing and Urban Development), update frequency (quarterly), and the return format (Katzilla envelope with data, quality scores, and citation details including a SHA-256 hash). This enhances transparency about data freshness, auditability, and output structure.

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 highly concise and well-structured. It front-loads the core purpose, specifies geographic levels and data source, then details the return format and quality metrics in a single, efficient paragraph. Every sentence adds value without redundancy, making it easy for an agent to quickly grasp the tool's function and output.

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, rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers the purpose, data source, update frequency, and return format, which complements the structured fields. The output schema likely details the Katzilla envelope, so the description need not explain return values further, making it adequately comprehensive.

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?

With 100% schema description coverage, the input schema already documents all parameters thoroughly. The description adds minimal parameter semantics, only implying geographic levels and year ranges without providing additional syntax or format details. It meets the baseline of 3 since the schema carries the full burden, but does not compensate with extra insights like examples or constraints 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 tool's purpose: 'Query the HUD Comprehensive Housing Affordability Strategy (CHAS) data. Provides housing affordability metrics at nation, state, or county level.' It specifies the exact resource (CHAS data) and the geographic levels, distinguishing it from sibling tools like 'housing__hud-fmr' or 'housing__hud-income-limits' by focusing on affordability metrics rather than fair market rents or income limits.

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 for usage: it's for querying housing affordability metrics at specific geographic levels (nation, state, county) from a U.S. government source with quarterly updates. However, it does not explicitly state when to use this tool versus alternatives like 'demographics__census-acs' or other housing-related tools, nor does it mention prerequisites or exclusions beyond the geographic scope.

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