semantic_search

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

No annotations are provided, so the description carries the behavioral burden. It usefully discloses the dependency on LLM_BASE_URL/LLM_MODEL and the expected OpenAI-compatible /v1/embeddings endpoint. It does not mention possible latency, cost, failure modes, or whether embeddings are computed on the fly, but for a search-style operation the disclosed dependency adds meaningful transparency.

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?

Three short sentences with the core purpose front-loaded and no wasted words. The only minor issue is the unexplained 'C3' marker, which adds noise without contributing to agent understanding.

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?

For a simple read-only semantic search tool with an output schema, the description covers the core purpose and a key external requirement. However, it omits any explanation of optional parameters like top_k and where, and provides no mention of expected behavior when the embedding service is unavailable.

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 clarifies that query is the semantic search phrase and text_column is the column being searched, but it gives no additional meaning for table_name, top_k, or where. Optional parameters are left entirely to inference from their names.

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 identifies a specific operation: semantic search over a text column via LLM embeddings. The clarifying phrase 'even if words do not match' makes the distinction from exact-match/SQL tools explicit, so an agent can tell this from siblings like sql_query.

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 appropriate use: when similarity in meaning is desired rather than exact keyword matches. However, it does not explicitly state when not to use this tool or name an alternative such as sql_query for exact matching, so usage guidance remains implied rather than explicit.

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