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 ('Climate TRACE (CC-BY 4.0)'), update frequency ('updates daily'), return structure ('Katzilla envelope'), and details about quality scores ('freshness/uptime/confidence') and citation ('source URL, license, SHA-256 data hash'). 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 states the core purpose and source, and the second details the return format and its components. Every sentence adds essential information (e.g., data source, update frequency, return structure, quality metrics, citation details) with zero wasted words, making it highly concise and front-loaded.

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 (4 parameters, read-only operation), the description is complete. It covers purpose, source, update frequency, return format, and behavioral context. With annotations handling safety profiles and an output schema presumably detailing the 'Katzilla envelope', the description provides all necessary context without redundancy, making it fully adequate for agent use.

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, clearly documenting all four parameters (country, sector, year, limit) with defaults, constraints, and examples. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining sector nuances or year lag implications. Given the high schema coverage, a baseline score of 3 is appropriate.

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's purpose: 'Get asset-level (facility/power plant) greenhouse gas emissions data from Climate TRACE.' It specifies the verb ('Get'), resource ('asset-level greenhouse gas emissions data'), and source ('Climate TRACE'), distinguishing it from sibling tools like 'environment__climate-trace' (likely aggregated data) and 'environment__climate-trace-sectors' (sector-level data).

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 usage by mentioning the data source, update frequency ('updates daily'), and return format ('Katzilla envelope'). However, it does not explicitly state when to use this tool versus alternatives (e.g., 'environment__climate-trace' or 'environment__climate-trace-sectors'), nor does it mention any prerequisites or exclusions, leaving some guidance gaps.

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