文本嵌入

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

No annotations are provided, so the description carries full burden. It only states basic functionality and OpenAI compatibility, without disclosing behavioral traits like rate limits, idempotency, input size constraints, or return format. For a tool generating embeddings, return format (e.g., array of floats) is crucial but omitted.

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 very short (two sentences) and front-loaded with the core purpose. It uses bullet-like clarity but lacks structured sections. Every sentence adds value, but could be more efficient by combining model info and input type into one line.

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?

With no output schema, the description should explain what the tool returns (e.g., embedding vectors). It does not. Given the tool's complexity (two parameters, straightforward return), the description is incomplete as it omits both output format and behavioral details like error handling or maximum input length.

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% with basic descriptions, but the description adds concrete model ID examples (text-embedding-3-small, jina-embeddings-v3) and clarifies input as string or array. This provides practical value beyond the schema alone.

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 tool name 'create_embeddings' and description '生成文本向量嵌入' clearly state the action (generate) and resource (text vector embeddings). The description explicitly mentions OpenAI compatibility, which distinguishes it from other generation tools like generate_audio or generate_image. It's specific and non-tautological.

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?

No guidance is provided on when to use this tool over siblings (e.g., ask_model for semantic search, or generate_document for text output). The description does not mention prerequisites, exclusions, or preferred contexts. The agent is left to infer usage from the tool's name alone.

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