embedding_search

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

Annotations already declare the tool as read-only, non-destructive, idempotent, and open-world. The description adds valuable behavioral details: it uses OpenAI embeddings via environment variables, infers embedding dimension from the schema, and returns results sorted by similarity. This goes beyond annotations but does not cover error specifics or rate limits.

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 opens with a one-sentence summary of the core functionality, then provides process details, environment setup, parameter overview, and return value. Every sentence adds diagnostic value without 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?

Given the tool's moderate complexity, the description covers the essential workflow, prerequisites (env vars), input parameters, and return type. It lacks explicit error handling details, but the output schema exists and the description mentions error returns, making it sufficiently complete for an agent to use correctly.

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?

The input schema has parameter descriptions, but the top-level schema description coverage is 0%. The tool description compensates by explaining the embedding process, environment variable requirements, and the overall workflow, adding meaning beyond the schema for critical parameters like 'query_text' and 'field_name'.

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 the tool converts a natural language query to a vector and performs similarity search. It distinguishes itself from sibling tools like 'vector_query' and 'generate_dense_embedding' by labeling itself as the 'high-level search interface', making its role and scope immediately clear.

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 positions this tool as the 'high-level search interface' for natural language queries, implying it should be used for general semantic search. However, it does not explicitly state when to avoid it or mention specific alternatives among siblings like 'multi_vector_query' or 'vector_query', leaving some ambiguity.

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