Katzilla MCP

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

The description adds valuable behavioral context beyond annotations: it discloses the real-time update nature, the specific return format (Katzilla envelope with data, quality, citation), and details about quality scores and citation contents (source URL, license, SHA-256 hash). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description effectively supplements with operational details.

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, followed by filter details, source information, and return format specifics. Every sentence adds value without redundancy, and it efficiently conveys necessary information in a compact form, making it highly concise and well-structured.

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 (read-only, non-destructive, idempotent, open-world), 100% schema coverage, and the presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits, and return format, leaving no significant gaps for the agent to understand and 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?

The description mentions filter parameters (state, year, number of results), but the input schema already provides 100% coverage with detailed descriptions for each parameter. The description does not add significant semantic meaning beyond what the schema specifies, such as explaining interactions between parameters or typical use cases, 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 specific verbs ('Query FEMA disaster declaration summaries') and resources ('FEMA disaster declaration summaries'), and distinguishes it from sibling tools by specifying its unique domain (hazards/disasters) and data source (FEMA). It avoids tautology by explaining what the tool does beyond the name/title.

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 by specifying filter parameters (state, year, number of results) and the real-time nature of updates. However, it does not explicitly state when not to use this tool or name specific alternatives among the sibling tools (e.g., hazards__fema-nfip-claims), which prevents a score of 5.

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