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 notes the data source ('Data.gov (Public Domain)'), update frequency ('updates daily'), and details the return structure ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation contents. 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 front-loaded with the core purpose, followed by usage guidance, source details, and return format explanation. Each sentence is necessary and contributes to understanding, with no wasted words, making it highly efficient and well-structured.

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 (data retrieval with quality metrics), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, usage prerequisites, source information, update frequency, and return structure details, providing all necessary context for an agent to 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, clearly documenting the single required parameter 'dataset_id'. The description adds minimal semantic context by mentioning 'Data.gov dataset ID (CKAN package UUID) from catalog search', which aligns with the schema but doesn't provide significant additional meaning. This meets 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: 'Download and serve any validated Data.gov dataset as structured JSON.' It specifies the action ('download and serve'), resource ('Data.gov dataset'), and output format ('structured JSON'), distinguishing it from sibling tools like 'datagov' (search) and other government data tools by focusing on dataset retrieval.

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 explicit usage guidance: 'Use the datagov action to search for datasets and get their IDs first.' It names the alternative tool ('datagov') and specifies the prerequisite step, clearly indicating when to use this tool versus others.

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