ask

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

Since annotations are absent, the description carries the full burden. It explains that the tool uses the server's LLM to synthesize an answer from beliefs and source documents, and that it is slower. This provides good insight into behavior, though it does not explicitly state whether the operation is read-only or if any side effects exist.

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 concise, with no unnecessary words. It is front-loaded with the main purpose, then provides behavioral context, and finally lists arguments. Every sentence adds value, making it efficient for an AI agent to parse.

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 simplicity (2 parameters, no nested objects) and the presence of an output schema, the description covers the essential aspects. It explains the tool's function, behavior, and sibling differentiation. However, it could mention any prerequisites like active knowledge base or authentication requirements.

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 description coverage is 0%, so the description must compensate. It adds basic semantics for each parameter: 'question: The question to ask' and 'project: Project name or UUID (uses default if empty)'. This is minimal but adequately clarifies the parameters beyond the schema titles.

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: 'Ask a question and get an LLM-synthesized answer grounded in the knowledge base.' It uses a specific verb (ask) and resource (knowledge base answer), and distinguishes itself from sibling 'deep_search' by noting it is slower but returns a ready-to-use answer.

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 a clear comparison with 'deep_search', stating that 'ask' is slower but returns a ready-to-use answer. This gives implicit guidance on when to use each. However, it does not explicitly mention when not to use 'ask' or compare to other siblings like 'search' or 'get_belief'.

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