semantic_search

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

With no annotations available, the description carries the full burden and does well: it reveals the vector-embedding mechanism, the semantic rather than lexical matching behavior, the similarity score in results, and implicitly the requirement of the `rag` extra. It does not explicitly state failure behavior if `rag` is missing, but overall it is transparent for a read-only search tool.

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 concise and well-structured: it front-loads the core distinction, then provides usage guidance, a comparative alternative, and the return characteristic. Every sentence earns its place with no filler.

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 low parameter count and the presence of an output schema, the description covers the essential context: what semantic search does, when to use it, when not to, and what kind of results to expect. Minor omissions like explicit `limit`/`min_similarity` semantics and the explicit `rag` prerequisite are present but not fully detailed.

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 0%, so the description must compensate. It effectively explains the meaning of `query` by describing semantic intent and hints at `min_similarity` through the mention of similarity scores. However, it does not explicitly describe `min_similarity` as a threshold or explain `limit`, leaving some parameter semantics to inference from names and defaults.

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 that the tool performs semantic search over vacancies using vector embeddings, not exact word matching, and returns vacancies with similarity scores. It explicitly distinguishes itself from search_vacancies, making the tool's unique purpose unmistakable.

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 gives explicit guidance on when to use this tool: when the query describes a task in one's own words or searches by concept. It also provides a clear alternative for exact-term search, search_vacancies, and explains why that alternative is preferable (faster, no extra `rag` dependency).

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