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 annotations: it discloses the return format (Katzilla envelope with data, quality, citation), explains quality scoring (freshness/uptime/confidence), and details citation contents (source URL, license, SHA-256 hash). This enriches the agent's understanding of the tool's output 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 efficiently structured: it opens with the core purpose, lists key data fields, states usage context, specifies source and update frequency, and details the return format—all in three dense sentences with zero wasted words. Each sentence adds critical information, making it highly concise and front-loaded.

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 (data retrieval with quality metadata), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by the return format description), the description is complete. It covers purpose, usage, source, update cadence, and detailed output behavior, leaving no significant gaps for the agent to operate effectively.

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 each parameter (state, year, limit) well-documented in the schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining interactions between parameters or default behaviors. Baseline 3 is appropriate when the schema fully covers parameter details.

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: retrieving 'National Flood Insurance Program (NFIP) claims data from FEMA' with specific data fields listed (loss amounts, property types, flood zones, dates of loss). It clearly distinguishes from sibling tools like 'hazards__fema-disasters' by focusing on flood insurance claims rather than disaster declarations or other hazard data.

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 ('useful for flood risk assessment and disaster analysis') and specifies the data source and update frequency. However, it does not explicitly state when to use this tool versus alternatives (e.g., other FEMA or hazard-related tools) or any prerequisites, which prevents a perfect score.

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