recommend_config

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

No annotations are provided, so the description must carry the full burden. It discloses the use of dummy embeddings by default, but does not mention side effects, idempotency, or other behavioral traits like whether it modifies state or runs asynchronously.

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 extremely concise at one sentence, but at the cost of missing critical information. It is structured well enough for a simple statement, but could be expanded to include essential details without becoming verbose.

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 7 parameters, no output schema, and no annotations, the description is severely incomplete. An agent cannot determine what the tool returns, how to set parameters correctly, or what the expected behavior is beyond a vague 'tuner' operation.

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 0% schema description coverage, the description should explain parameters. It does not mention path, top_k, max_docs, use_case, strategies, content_type, or embedding_model, leaving all semantics to be inferred from parameter names 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 says 'Run tuner and return ranked Recommendation', which vaguely states a tuning action but does not define what a 'tuner' or 'Recommendation' is in this context. The phrase 'uses dummy embeddings by default' adds some specificity but the core purpose remains unclear.

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?

No explicit guidance on when to use this tool versus its siblings (evaluate_chunking, list_strategies, preview_chunks). The description does not mention alternatives or conditions for use, leaving the agent to infer.

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