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 this: it discloses the update frequency ('updates monthly'), describes the return format ('Katzilla envelope { data, quality, citation }'), and explains quality metrics and citation details. This enriches the agent's understanding 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 efficiently structured in two sentences: the first covers purpose, scope, and data details; the second explains the return format and its components. Every sentence adds critical information (e.g., source, update frequency, output structure) with zero waste, making it easy for an agent to parse quickly.

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 (economic data retrieval), the description is complete: it covers purpose, data source, coverage, update frequency, and output format. With annotations handling safety/behavioral traits and an output schema presumably detailing the 'Katzilla envelope', the description provides all necessary contextual information without redundancy, ensuring the agent can use the tool effectively.

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 with detailed enum explanations (e.g., geo codes, unit meanings, indicator mappings). The description does not add further parameter semantics beyond what the schema provides, such as examples of valid geo codes or clarification on unit differences. With high schema coverage, a baseline score of 3 is appropriate as the description relies on the schema for param 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 retrieves 'GDP and main aggregates from Eurostat national accounts', specifying the data source (Eurostat), coverage (EU/EEA member states), frequency (quarterly/annual), and key metrics (GDP, household consumption, etc.). It clearly distinguishes from siblings like economic__eurostat-inflation or economic__eurostat-unemployment by focusing on GDP and related aggregates.

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 Eurostat GDP data across EU/EEA states. It implies usage for economic analysis but does not explicitly state when not to use it or name specific alternatives among siblings (e.g., economic__bea-gdp for US data). The mention of 'Source: Eurostat' and coverage details offers good guidance without explicit exclusions.

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