generate_dense_embedding

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and openWorldHint=true, so the safety profile is clear. The description adds return format details (JSON with preview, model, dimension, vector) but does not disclose behavioral traits like rate limits, cost implications, or fallback behavior when env vars are missing, which would be valuable given the reliance on an external API.

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 well-structured with an opening sentence stating the purpose, followed by an Args list and return type. It is appropriately sized for the complexity of the tool, though the bullet list could be slightly more concise. No redundant information is present.

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 provides a complete picture of the tool's behavior: what it does, how to use it (parameters with defaults and env var fallbacks), and what it returns (JSON with specific fields). No output schema is provided, so the description compensates fully. The tool is simple and the description covers all necessary information for correct invocation.

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?

Despite schema description coverage being 0% (schema has descriptions for each parameter), the tool description adds meaningful context beyond the schema by summarizing each parameter with defaults and env var fallback (e.g., 'api_key: OpenAI API key (or OPENAI_API_KEY env var)'). This helps an agent understand the parameter semantics without inspecting the schema.

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 generates a dense embedding vector from text using OpenAIDenseEmbedding, with explicit mention of converting text into a fixed-length vector for similarity search. This distinct purpose is well differentiated from sibling tools like embedding_search or vector_query, which use embeddings rather than generate them.

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 use for generating embeddings for similarity search but does not explicitly state when to use this tool versus alternatives like embedding_search or vector_query. No exclusion criteria or comparison to siblings is provided, leaving the agent to infer usage without clear guidance.

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