Katzilla MCP

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

The description adds valuable behavioral context beyond annotations: it specifies that the data is free, real-time, and requires no API key, and details the return format (Katzilla envelope with quality scores and citation for audit). Annotations already indicate read-only, non-destructive, idempotent, and open-world hints, so the description complements this with practical usage details without contradiction, earning a high score for transparency.

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 in the first sentence, followed by key details like supported chains, no API key, source, and return format. Each sentence adds essential information without redundancy, making it efficient and well-structured for quick understanding by an AI agent.

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 (6 parameters, real-time data across multiple chains), the description is complete: it covers the purpose, data source, key features (free, no API key), supported chains, return format, and auditability. With annotations providing safety hints and an output schema existing (though not detailed here), the description adequately supplements the structured data without needing to explain return values, ensuring the agent has sufficient context.

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 fully documents all 6 parameters, including enums and defaults. The description does not add parameter-specific semantics beyond what the schema provides, such as explaining how 'networkId' maps to chains or 'action' affects outputs. However, it implies parameter usage by mentioning data types like pools and token details, aligning with the baseline score when schema coverage is high.

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: fetching free decentralized exchange (DEX) and DeFi data from DexPaprika, with specific examples like liquidity pools, token prices, OHLCV charts, and 24h trade volume. It clearly distinguishes from sibling tools by specifying the data source (DexPaprika) and the types of cryptocurrency/blockchain data it provides, which is unique among the listed crypto siblings (e.g., coinpaprika, etherscan, blockchain-stats).

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 real-time DEX/DeFi data across multiple chains, with no API key required. It implies usage by mentioning the supported chains (Ethereum, Solana, etc.) and data types, but does not explicitly state when not to use it or name specific alternatives among siblings, such as using crypto__coinpaprika for broader cryptocurrency data instead of DEX-specific data.

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