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 behavior, but the description adds valuable context beyond this. It specifies the return format ('Katzilla envelope { data, quality, citation }'), explains quality scores ('freshness/uptime/confidence'), and details citation components ('source URL, license, SHA-256 hash'), which are not covered by annotations. No contradiction with annotations is present.

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 and front-loaded, starting with the core purpose, followed by source details and return format explanation. Every sentence adds value without redundancy, making it easy to parse quickly while providing comprehensive information in a compact form.

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 (drug lookup with quality metrics), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, return structure, and quality aspects, leaving no significant gaps for an AI agent to understand and invoke the tool effectively without needing to rely solely on structured fields.

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 single parameter 'name' well-documented in the schema. The description does not add any additional meaning or examples beyond what the schema provides (e.g., it doesn't clarify search behavior like partial matches or case sensitivity). Given the high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate with extra parameter insights.

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 specific verbs ('look up drug information') and resources ('from NLM RxNorm'), listing exact information types (brand names, generic names, dosage forms, RxCUI identifiers). It distinguishes itself from sibling tools by specifying its domain (health/medical drug data) and source (NLM RxNorm), unlike other health tools like CDC data or clinical trials.

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 ('look up drug information from NLM RxNorm') and mentions the source and update frequency, which helps in understanding its scope. However, it does not explicitly state when not to use it or name specific alternatives among sibling tools, such as other health data sources like PubChem or FDA data, leaving some ambiguity in tool selection.

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