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), update frequency (monthly), and details about the return format (Katzilla envelope with quality scores and citation), which helps the agent understand data freshness and auditability.

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, and the second explains the return format and its components. Every sentence adds essential information without redundancy, making it front-loaded and easy to parse.

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 with parameters), rich annotations (read-only, idempotent, etc.), 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 return structure, leaving no significant gaps for agent understanding.

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 schema fully documents the 'query' and 'limit' parameters. The description does not add any parameter-specific details beyond what the schema provides, such as query examples or limit constraints, so it meets the baseline for adequate coverage.

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 specific action ('Search'), resource ('USDA FoodData Central database'), and content ('food and nutrient information'), distinguishing it from sibling tools like agriculture__usda-ers or agriculture__usda-nass by focusing on food data rather than economic or 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?

It implicitly suggests usage for food and nutrient queries, with context about the data source and update frequency, but does not explicitly state when to use alternatives or exclude specific scenarios, such as non-food searches or real-time data needs.

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