knowledge_ask

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

The description adds valuable behavioral context beyond the annotations by disclosing 'source citations' (critical for verifying AI-generated answers), the underlying 'Weaviate AsyncQueryAgent' technology (hinting at async behavior), and the specific return type ('KnowledgeAskResult schema'). It complements the safety annotations (readOnly/destructive hints) with functional transparency about how the answer is constructed.

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 a clear front-loaded purpose statement followed by implementation details and a docstring-style Args/Returns breakdown. While the Args/Returns sections are somewhat redundant given the complete input schema and existing output schema reference, they do not significantly detract from clarity.

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?

For a tool with 2 parameters, full schema coverage, and an existing output schema, the description is appropriately complete. It references the 'KnowledgeAskResult' output schema by name and highlights the critical 'source citations' feature. It could be improved by mentioning behavior when no relevant knowledge is found, but this is not critical given 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?

With 100% schema description coverage, the baseline is 3. The description lists the parameters in the Args section ('Natural language question', 'Which collections to search'), but this largely mirrors the schema's own descriptions ('Question to answer from stored knowledge', 'Collections to search'). No additional semantic context (e.g., query syntax tips, collection selection strategies) is provided.

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 core action ('Ask a question and get an AI-generated answer') and resource ('stored knowledge'). It effectively distinguishes from sibling tools like 'knowledge_search' and 'knowledge_query' by specifying 'ask mode' and emphasizing that it 'synthesizes an answer' rather than returning raw records.

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 through phrases like 'Natural language question' and the 'ask mode' implementation detail, indicating this is for generative Q&A over retrieval. However, it lacks explicit guidance on when to prefer this over siblings like 'knowledge_search' (keyword retrieval) or 'knowledge_query' (structured filtering), and provides no exclusion criteria.

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