search_knowledge_file

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

The annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds one meaningful behavioral trait—that no DeepTutor model call is made—but does not disclose limit defaults, case sensitivity, or error behavior when no matches 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?

A single, front-loaded sentence states the core behavior and the critical non-behavior ('no DeepTutor model call'). Every word earns its place, and the description is neither padded nor underspecified.

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 100% schema parameter coverage, readOnly annotations, and an existing output schema, the description provides the one crucial contextual distinction—literal search without model invocation. Minor missing details like result limits or search semantics are already handled by the schema and output schema, so nothing essential is absent.

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 each parameter is already documented in the schema. The description reinforces that 'query' is a literal phrase, matching the schema's 'Literal phrase to find', but contributes little meaning beyond what the schema already states. Baseline 3 is appropriate.

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 names a specific verb ('find'), a specific resource ('one knowledge base file'), and the exact behavior ('literal text matches'). It also explicitly distinguishes itself from a model-based search via 'no DeepTutor model call', which differentiates it from search_vector_knowledge without needing to inspect that sibling.

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 the tool is for exact literal search within a single file, and the phrase 'no DeepTutor model call' hints that it is the cheaper/non-semantic alternative. However, it does not explicitly state when to choose this over read_knowledge_file or search_vector_knowledge, leaving usage conditions to inference.

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