Katzilla MCP

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

The description adds valuable behavioral context beyond the annotations. While annotations indicate read-only, non-destructive, idempotent, and open-world traits, the description specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality metrics ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 data hash'), which are not covered by annotations. No contradictions with annotations exist.

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 essential details about the source, update frequency, and return format in a compact manner. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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 (search with multiple filters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by the description of the return format), the description is complete. It covers the purpose, data source, behavioral traits, and output structure, leaving no significant gaps for an agent to understand and use the tool 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?

With 100% schema description coverage, the input schema already fully documents all 5 parameters (query, office, cycle, page, limit) with descriptions, enums, and constraints. The description does not add any parameter-specific semantics beyond what the schema provides, such as example queries or advanced usage tips, 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 ('Search for') and resource ('U.S. federal election candidates from the Federal Election Commission (FEC) API'), distinguishing it from sibling tools that focus on other government or public data domains like agriculture, crime, or economic data. It explicitly identifies the data source and scope.

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 by specifying the data source (FEC API) and update frequency (daily), which helps users understand when to use this tool for current federal election candidate data. However, it does not explicitly mention when not to use it or name specific alternatives among the many sibling tools, such as other government data tools like 'government__congress-bills' or 'government__sec-edgar'.

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