ai_knowledge_document

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

No annotations are provided, so the description must disclose behavioral traits. It explains the CRUD nature and lists methods, but lacks details on side effects, idempotency, error handling, rate limits, or what happens on mutation operations. For a complex tool with 19 parameters, this is insufficient.

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 well-structured, front-loading the legacy status and alternative recommendation, then listing methods. It is concise yet informative, though the methods list could be slightly more compact. Overall, it earns its length.

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 high parameter count, nested objects, and multiple methods, the description provides a general overview but lacks specifics on how each method uses parameters, expected return values, or error scenarios. It is adequate for a high-level understanding but not fully complete for an agent to invoke correctly without additional inference.

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?

Input schema has 100% description coverage, so baseline is 3. The description reiterates the method parameter and required API key, but adds no new meaning or context for the other 17 parameters. It does not explain parameter interdependencies or usage patterns beyond the schema.

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 it is a legacy AI Knowledge API for document CRUD operations and lists the available methods. It distinguishes itself from siblings by marking it as legacy and directing to use Storage files/vector_stores APIs for new RAG data, but does not explicitly compare to sibling tools.

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 implies usage for legacy AI Knowledge document management and mentions the need for a legacy project API key. However, it does not explicitly specify when to use or avoid this tool relative to siblings, nor provide clear alternative recommendations beyond the storage APIs.

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