semantic_search

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

With no annotations, the description fully explains the behavioral trait of running an ONNX embedding model locally as WASM without external APIs. This is good transparency. However, it omits potential limitations like language restrictions or performance characteristics.

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 consists of two compact sentences, front-loading the core purpose (semantic search) and then providing technical detail (local ONNX/WASM execution). No filler or redundancy.

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?

The description explains the search mechanism but does not describe what the tool returns (e.g., similarity scores, ranked documents). Since there is no output schema, this is a notable gap. The document parameter structure is detailed in schema, so that part is adequate.

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 coverage is 100% (all 3 parameters described). The description adds minimal additional semantic value beyond the schema: it mentions 'natural language question' for query and 'documents/files list' for documents, which are already in schema descriptions. 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 clearly specifies it performs semantic search on source code or documents using natural language queries. It distinguishes the local execution via ONNX/WASM, setting it apart from any API-based search. No sibling tools perform similar semantic search, so purpose is unambiguous.

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 does not explicitly state when to use this tool versus alternatives or when not to use it. While sibling tools are largely unrelated, some could be confused (e.g., process_dataframe for data processing). No guidance on preferred contexts or prerequisites is provided.

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