Katzilla MCP

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

The description adds valuable behavioral context beyond annotations: it specifies the data source (U.S. Department of Housing and Urban Development), update frequency (quarterly), and return format (Katzilla envelope with quality scores and citation details). Annotations already indicate read-only, non-destructive, idempotent, and open-world hints, so the description complements this by explaining data freshness and auditability without contradiction.

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 well-structured and concise, with two sentences that efficiently cover purpose, context, source, updates, and return format. Every sentence adds essential information without redundancy, making it easy to scan and understand the tool's key aspects.

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, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema (implied by the return format description), the description is complete. It covers purpose, usage context, behavioral traits, and output details, leaving no significant gaps for an agent to invoke the tool correctly.

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 fully documents the three parameters (stateCode, entityId, year) with descriptions, enums, defaults, and constraints. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining the relationship between stateCode and entityId in more detail, so it 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 tool's purpose with a specific verb ('Get'), resource ('income limit data from the U.S. Department of Housing and Urban Development'), and context ('determine eligibility for HUD assisted housing programs by metro area'). It distinguishes itself from sibling tools by focusing on HUD income limits, which is unique among the listed siblings that cover various domains like agriculture, consumer, crime, etc., with no other HUD-specific tools.

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 this tool: to retrieve income limit data for HUD programs, with source and update frequency mentioned. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as other housing tools like 'housing__hud-chas' or 'housing__hud-fmr', which might serve different purposes.

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