ai_knowledge_completion

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

No annotations are provided, so the description carries full burden. It mentions legacy status, required API key, and that streaming is not recommended for MCP. However, it does not disclose rate limits, error behavior, response format, or side effects beyond the method descriptions. This is adequate but not thorough.

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 a clear summary, then enumerates methods, and includes a highlighted important note. It is moderately concise; every sentence serves a purpose, though some details could be tightened.

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 12 parameters, 4 methods, and no output schema or annotations, the description is fairly complete. It explains tool purpose, usage, prerequisites, and the models pre-check. Absence of return value details is a minor gap, but overall adequate for the complexity.

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?

Schema coverage is 100% with all parameters described. The description adds context beyond the schema by explaining the methods and providing an important note about the 'models' method. This adds value, raising it above the baseline of 3.

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 is a legacy AI Knowledge API for direct LLM completion without RAG. It lists distinct methods (chat, openai, embeddings, models) and contrasts with the newer LLM Gateway, making the purpose and scope immediately clear.

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?

Explicitly directs users to use LLM Gateway for new direct model calls, providing an alternative. Also includes an important usage rule: before changing a model, call with method='models' to verify availability. This gives clear when-to-use and when-not-to-use guidance.

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