search_vector_knowledge

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

Annotations already declare read-only and non-destructive behavior. The description adds useful behavioral detail beyond that: it 'Uses the configured query embedding' and explicitly disclaims conversation/answer synthesis, giving the agent a clearer model of what happens at invocation.

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?

Two compact sentences that front-load the primary action and then add the key exclusions. Every phrase earns its place, with no filler or redundant restatement of schema fields.

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?

With full parameter schema, output schema, and annotations available, the description covers the essential role and boundaries of the tool. Nothing an agent needs to decide whether to invoke it or to call it correctly is missing.

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 100%, so the schema already documents all parameters. The description only reinforces that kb_name selects a knowledge base and that query is a search phrase, adding no substantive parameter-level meaning beyond what the schema provides.

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?

Specifies a concrete action and resource: 'Retrieve original indexed passages from one DeepTutor LlamaIndex knowledge base.' It also distinguishes itself from conversation/answer tools via 'does not start a DeepTutor conversation or synthesize an answer,' which differentiates from siblings like send_message_to_deeptutor.

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?

Provides clear context by stating it retrieves raw passages rather than synthesizing answers, giving an implicit when-not-to-use. However, it does not explicitly name sibling alternatives such as search_knowledge_file or state when one should choose that tool over this one.

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