Katzilla MCP

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

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it discloses the source ('College Scorecard – U.S. Department of Education'), update frequency ('updates annual'), and detailed return structure ('Katzilla envelope { data, quality, citation }') with quality metrics and audit features. This enriches behavioral 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 tool's purpose and source, and the second details the return format and its components. Every sentence adds critical information (e.g., data source, update frequency, return envelope structure) with zero wasted words, 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 moderate complexity (2 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and return structure, compensating for areas where structured data might be sparse (e.g., behavioral context like data freshness and audit trails). With annotations and output schema handling safety and return values, the description fills remaining gaps 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 both parameters ('state' and 'limit') fully documented in the input schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining interactions between parameters or typical use cases. The baseline score of 3 is appropriate since the schema carries the full parameter documentation burden.

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: 'Query school demographic data from the College Scorecard API including race/ethnicity breakdowns by state.' It specifies the verb ('query'), resource ('school demographic data'), source ('College Scorecard API'), and scope ('by state'), and distinguishes it from its sibling 'education__college-scorecard' by focusing specifically on demographic breakdowns rather than general scorecard 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 implies usage context by mentioning the data source and update frequency ('updates annual'), but does not explicitly state when to use this tool versus alternatives like its sibling 'education__college-scorecard' or other demographic tools. It provides some operational context but lacks explicit guidance on selection criteria or exclusions.

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