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openai_embeddings_create

Generate vector embeddings from text input to represent semantic meaning for similarity search, clustering, and AI applications using OpenAI-compatible models.

Instructions

Create embeddings (POST /v1/embeddings).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "input": "<input>"
    },
    "name": "openai_embeddings_create"
  }
}

Input Schema

Name	Required	Description	Default
`input`	Yes
`model`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 4, 2025

TDQS

C2.1/5.0

Behavior1/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states the action and endpoint, failing to describe critical aspects such as authentication needs, rate limits, response format, or potential side effects (e.g., if it's a read-only or mutating operation). This leaves significant gaps in understanding how the tool behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with a single sentence, front-loaded with the key action. There is no wasted text, making it efficient in structure, though this brevity contributes to gaps in other dimensions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of creating embeddings (a mutating operation with parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks essential details about behavior, parameters, and outputs, making it insufficient for an AI agent to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning the input schema provides no descriptions for parameters. The description does not add any meaning beyond the schema, failing to explain what 'input' and 'model' parameters represent, their expected formats, or examples. With 2 parameters and no compensation in the description, this is inadequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('Create embeddings') and the API endpoint ('POST /v1/embeddings'), which clarifies the verb and resource. However, it lacks specificity about what embeddings are or how they differ from sibling tools like chat completions or model listing, making it somewhat vague in distinguishing its unique purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like openai_chat_completions or openai_models_list. It does not mention any context, prerequisites, or exclusions, leaving the agent without clear usage instructions.

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

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