ai_knowledge_project

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

No annotations are provided, so the description carries full burden. It explains auth requirements and legacy status but does not disclose potential side effects (e.g., deletion irreversibility, update impacts), rate limits, or error conditions. For a tool with multiple methods and parameters, more behavioral context would be beneficial.

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 with clear sections for legacy context, auth methods, and method lists. It is front-loaded with the key message. While it is relatively long, it earns its length by providing necessary differentiation. Minor redundancy could be trimmed, but overall it is concise.

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 complexity (17 params, multiple methods, two auth modes), the description covers auth and method groupings well. However, it lacks details on return values (no output schema), error handling, and specific behavioral outcomes for each method. The 'create' method mentions returning an apiKey, but other methods are less described. Some gaps remain.

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?

All 17 parameters have schema descriptions (100% coverage). The description adds significant value by grouping parameters by auth method (apiKey vs Bearer token) and listing which params are required for each method. It clarifies the method enum and explains the ai nested object. This goes beyond the schema alone.

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 for 'Legacy AI Knowledge API: project/agent management' and lists the supported methods. It distinguishes itself from the newer Agent Factory APIs. While the name and sibling tools like ai_knowledge_completion and ai_knowledge_document suggest the domain, the description could more explicitly contrast with these sibling tools, but overall purpose is 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?

The description explicitly separates methods requiring apiKey from those using Bearer token, and specifies when workspaceName is needed. It advises against using this legacy API for new agents, directing to Agent Factory. It does not directly compare with sibling tools, but the operational context (project management vs. completions/documents) is implicitly clear.

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