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, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the update frequency ('updates monthly'), describes the return structure ('Katzilla envelope { data, quality, citation }'), and explains quality and citation details, which aids in understanding data reliability and auditability 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 coverage, source, update frequency, and return format in a single, efficient sentence. Each part adds value without redundancy, and the structure is logical, making it easy to parse for an AI agent. No sentences are wasted on repeating schema or annotation information.

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 (5 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, source, update frequency, and return structure, which, combined with structured fields, provides sufficient context for an agent to use the tool effectively. The output schema handles return values, so the description need not explain them further.

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%, with each parameter well-documented in the schema (e.g., indicator codes, economy codes, year format). The description does not add parameter-specific semantics beyond what the schema provides, such as explaining indicator code meanings or usage examples. However, it implies parameter relevance through the coverage domains, maintaining the baseline score for high schema 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 tool's purpose with specific verb ('Fetch') and resource ('international trade statistics from the World Trade Organization (WTO) API'), and distinguishes it from siblings by specifying coverage domains ('merchandise trade, services, and tariffs') and source ('World Trade Organization (Public)'). This provides precise differentiation from other economic tools like economic__comtrade or economic__world-bank.

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 context through the coverage domains and source, but does not explicitly state when to use this tool versus alternatives like economic__comtrade or international__wits-trade. It mentions the return format and quality metrics, which helps understand the tool's output, but lacks explicit guidance on selection criteria or prerequisites.

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