Katzilla MCP

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

The description adds valuable behavioral context beyond annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world traits, but the description discloses the return format ('Katzilla envelope { data, quality, citation }'), data quality metrics ('freshness/uptime/confidence'), and audit features ('SHA-256 data hash'). This enriches the agent's understanding 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 efficiently structured in two sentences: the first states the core functionality and data returned, and the second details the output format, quality metrics, and source information. Every sentence adds essential value without redundancy, making it front-loaded and appropriately sized.

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 API with structured output), the description is complete. It covers purpose, data source, update frequency, and detailed output behavior. With annotations covering safety traits and an output schema presumably detailing the 'Katzilla envelope', no critical gaps remain for agent usage.

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 fully documents all three parameters (query, limit, fields). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining field selection strategies or query formatting nuances. Thus, it meets the baseline but doesn't enhance parameter understanding.

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: 'Search U.S. colleges and universities via the Department of Education College Scorecard API' with specific resources mentioned ('admissions rates, tuition costs, and enrollment size'). It clearly distinguishes from sibling tools by specifying the exact data source (College Scorecard API) and domain (education), unlike other education tools like 'education__ed-demographics' or 'education__hipolabs-universities'.

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: for searching U.S. colleges via the College Scorecard API with specific data types. It mentions the source and update frequency ('updates quarterly'), which helps guide usage. However, it does not explicitly state when not to use it or name specific alternatives among siblings, preventing a perfect score.

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