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 hints, covering safety and behavior. The description adds valuable context beyond annotations: it specifies the data source, update frequency ('updates monthly'), and details about the return structure ('quality scores freshness/uptime/confidence; citation carries the source URL, license, and a SHA-256 data hash for audit'), which aids in understanding reliability and auditability without contradicting 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 concise and well-structured in two sentences: the first states the purpose and source, and the second explains the return format and quality metrics. Every sentence adds essential information 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 low complexity (1 parameter, 100% schema coverage), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, and return structure, providing all necessary context for an AI agent to use the tool effectively without needing to explain return values, as the output schema handles that.

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 input schema has 100% description coverage, with the parameter 'name' documented as 'Name to predict gender for'. The description does not add further semantic details about the parameter, such as format examples or constraints, but since the schema already provides adequate information, a baseline score of 3 is appropriate.

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: 'Predict the gender of a person based on their name.' It specifies the verb ('predict'), resource ('gender'), and input ('name'), and distinguishes it from siblings by mentioning the data source 'Genderize (Free Tier)' and the specific return format 'Katzilla envelope', which is unique among the listed 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 usage by stating the source ('Genderize (Free Tier)') and update frequency ('updates monthly'), which helps in deciding when to use it. However, it does not explicitly mention when not to use it or name alternatives, such as other demographic tools like 'demographics__census-acs' or 'demographics__rest-countries', which could offer gender data in different contexts.

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