Creates text embeddings from provided text or JSON input, omitting vector values by default to keep MCP responses compact.
Create text embeddings. Vector values are omitted by default to keep MCP responses compact.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| dimensions | No | ||
| input_json | No | ||
| input_text | No | ||
| include_vectors | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden, and it does disclose one genuinely useful trait: vectors are omitted by default to keep responses compact, which explains the include_vectors default. It says nothing about what is returned instead (count, dimensions, usage), batching limits, cost, or whether inputs can be arrays.
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?
Two short sentences with zero filler, and the most surprising behavior (omitted vectors) is front-loaded immediately after the purpose statement. Nothing needs trimming.
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?
Five parameters with no schema descriptions and no annotations is a heavy documentation load, and the description addresses essentially none of it. The presence of an output schema excuses it from explaining return values, but the input ambiguity between input_text and input_json is a real gap for a tool whose entire job is accepting input text.
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, but it only indirectly touches include_vectors. The most confusing pair, input_text versus input_json (both default empty strings), is left entirely unexplained, as is dimensions defaulting to 0.
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?
"Create text embeddings" states a specific verb and resource, and the sibling list (chat completions, images, audio, moderation) makes the domain unambiguous. It stops short of naming the provider endpoint semantics or what kind of input is embedded, so it is clear but not maximally precise.
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?
There is no guidance on when to choose this over siblings such as openai_create_response or openai_create_chat_completion, and no prerequisites or constraints are given. The agent must infer usage entirely from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.