Katzilla MCP

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation contents ('source URL, license, SHA-256 hash'). This enriches the agent's understanding of output behavior and auditability.

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 additional context and output details. Every sentence adds value: explaining FMR usage, data source, update frequency, and return structure. It is efficiently structured without redundancy or unnecessary information.

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 (3 parameters, 100% schema coverage, annotations, and output schema), the description is complete. It covers purpose, usage context, behavioral traits beyond annotations, and output format. With an output schema present, the description appropriately focuses on high-level return structure without needing to detail all return values.

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%, with detailed descriptions for each parameter (e.g., 'U.S. state/territory code', 'HUD entity ID', 'FMR fiscal year'). The description does not add further parameter semantics beyond the schema, such as explaining the relationship between stateCode and entityId in more depth. Baseline 3 is appropriate as the schema adequately documents parameters.

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 explicitly states the tool's purpose: 'Get Fair Market Rent (FMR) data from the U.S. Department of Housing and Urban Development (HUD).' It specifies the verb ('Get'), resource ('FMR data'), and source ('HUD'), and distinguishes it from siblings by focusing on rental voucher payment amounts for metro areas, unlike other housing tools like 'hud-chas' or 'hud-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 when to use the tool: to retrieve FMR data for determining rental voucher payments. It mentions the data source and update frequency ('updates quarterly'), which helps in timing usage. However, it does not explicitly state when not to use it or name alternatives among siblings, such as for income limits or housing characteristics.

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