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. The description adds valuable context beyond this: it discloses the data source ('Nobel Prize API (CC0)'), update frequency ('updates daily'), and return structure ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation contents. 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 front-loaded with the core purpose, followed by source and return details. Every sentence adds value: the first states the query action, the second provides source and update info, and the third explains the return structure. It is efficiently structured with no wasted words.

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, rich annotations, and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It covers purpose, source, update frequency, and return format, which complements the structured data. No additional explanation of return values is needed due to the output schema.

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%, so the schema already documents all parameters (limit, year, category). The description mentions filtering by year and category but does not add significant semantic details beyond what the schema provides (e.g., it doesn't explain category codes like 'phy' or 'che' in more depth). Baseline 3 is appropriate as the schema handles most parameter documentation.

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 Nobel Prize data by year and category. Returns laureates and prize details.' It specifies the verb ('query'), resource ('Nobel Prize data'), and distinguishes it from siblings by focusing on Nobel Prize data specifically, 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 implies usage by mentioning filtering by year and category, but does not explicitly state when to use this tool versus alternatives (e.g., other science tools like arXiv or PubMed). It provides some context ('Science & Research Agent') but lacks explicit guidance on exclusions or comparisons with sibling tools.

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