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. The description adds valuable context beyond annotations: it specifies the data source (USDA FoodData Central, Public Domain), update frequency (daily), and details about the return format (Katzilla envelope with data, quality scores, and citation including SHA-256 hash). This enhances transparency about data freshness, auditability, and output structure.

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 purpose and source, the second details the return format and its components. Every sentence adds essential information (e.g., update frequency, output envelope structure), with no redundant or vague phrasing. It is front-loaded with the core functionality.

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 (search with two parameters), rich annotations (covering safety and behavior), and the presence of an output schema (implied by the description of the return format), the description is complete. It covers purpose, source, update frequency, and output details, providing sufficient context for an agent to use the tool effectively without needing to explain return values redundantly.

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, fully documenting the 'query' and 'limit' parameters. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't clarify query syntax or result ordering). With high schema coverage, the baseline is 3, as the description relies on the schema for parameter details.

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 the USDA FoodData Central database for nutrition information on foods.' It specifies the verb ('Search'), resource ('USDA FoodData Central database'), and what it returns ('nutrition information on foods'). It distinguishes from siblings by focusing on nutrition data, unlike other USDA tools (e.g., agriculture__usda-nass for agricultural statistics).

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 nutrition information from the USDA FoodData Central database. It mentions the source updates daily, implying it's suitable for current data needs. However, it does not explicitly state when not to use it or name specific alternatives among the many sibling tools, though the health domain context helps differentiate.

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