Katzilla MCP

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

Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the bar is lower. The description adds valuable context beyond annotations: it specifies the data source (GSA Site Scanning), update frequency ('updates daily'), and details the return structure ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation contents. It does not contradict annotations.

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 appropriately sized and front-loaded, with the core purpose stated first, followed by source details and return structure. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 (read-only data access with two parameters), rich annotations (covering safety and behavior), and the presence of an output schema (implied by the description of the return structure), the description is complete enough. It explains the data source, update frequency, and return format, compensating well for any gaps and aligning with the structured fields.

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%, providing full documentation for both parameters (limit and target_url). The description does not add any parameter-specific semantics beyond what the schema already states, such as explaining how target_url filtering works or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Access GSA Site Scanning data') and resources ('federal website health checks'), and distinguishes it from siblings by specifying the data source (GSA Site Scanning) and content types (DAP analytics, USWDS adoption, mobile readiness). It goes beyond a tautology by detailing what the data encompasses.

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 ('Access GSA Site Scanning data for federal website health checks'), but does not explicitly state when not to use it or name alternatives among the sibling tools. It implies usage for federal website health data, but lacks explicit exclusions or comparisons.

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