Katzilla MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable behavioral context beyond annotations: it discloses the data source (World Bank Development Indicators API), update frequency (monthly), licensing (CC BY 4.0), and detailed return structure (Katzilla envelope with quality scores and citation details). While annotations already cover read-only, non-destructive, idempotent, and open-world hints, the description enriches this with practical implementation details like data lag (~2 years) and audit features (SHA-256 hash).

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, and data source; the second details return format, quality metrics, and citation. Every element adds value without redundancy, and 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, rich annotations (read-only, idempotent, etc.), 100% schema coverage, and the presence of an output schema (implied by return format description), the description is complete. It covers purpose, usage context, behavioral traits, and output 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 input schema already fully documents all three parameters (country, year, indicator) with defaults and descriptions. The description adds marginal value by mentioning the data lag for year and listing example indicator IDs, but does not provide significant additional semantics beyond what the schema offers, meeting the baseline 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 verbs ('Query World Bank trade indicators') and resources ('exports, imports, trade balance'), and distinguishes it from siblings by specifying the exact data source (World Bank Development Indicators API). It explicitly mentions the return format (Katzilla envelope), which helps differentiate it from other economic/trade tools in the sibling list.

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 ('by country and year'), mentions the data source and update frequency, and implies usage for trade indicator queries. However, it does not explicitly state when not to use it or name alternative tools (e.g., economic__world-bank, trade__eurostat-trade from the sibling list), which prevents a perfect score.

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