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 return format ('Katzilla envelope { data, quality, citation }'), explains quality scoring ('freshness/uptime/confidence'), and describes citation details including audit features. No contradiction with annotations exists.

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 covers purpose, parameters, source, and update frequency; the second explains the return format and its components. Every element adds value without redundancy, making it front-loaded and zero-waste.

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 (covering safety and idempotency), 100% schema coverage, and the presence of an output schema (implied by the return format description), the description is complete. It adds necessary context like data source, update cadence, and return structure without needing to repeat what's already in 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?

Schema description coverage is 100%, so the schema already fully documents all three parameters. The description mentions filtering 'by year, report, and state' but doesn't add syntax, format, or semantic details beyond what the schema provides (e.g., it doesn't explain report options like 'crop_production_practices' or state abbreviation rules). Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Query the USDA ARMS API'), resource ('farm business economics data'), and scope ('by year, report, and state'). It distinguishes from sibling tools like agriculture__usda-nass and agriculture__usda-fooddata by specifying the ARMS survey focus on farm economics rather than other agricultural data types.

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 farm business economics data') and mentions the data source and update frequency ('USDA ERS, updates monthly'). However, it doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools, though the specificity helps differentiate.

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