MCP Tooling Lab

embed_text

Generate OpenAI embeddings for text arrays to enable semantic search and document indexing in RAG pipelines.

Instructions

Generate OpenAI embeddings for an array of texts.

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": {
      "texts": "<texts>"
    },
    "name": "embed_text"
  }
}

Input Schema

Name	Required	Description	Default
`texts`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jan 15, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the basic function but doesn't cover important traits such as rate limits, authentication needs, error handling, or what the embeddings represent (e.g., model used, dimensions). This leaves significant gaps for a tool that likely interacts with external APIs.

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 a single, efficient sentence with no wasted words. It is front-loaded with the core action and resource, making it easy to parse quickly, which is ideal for conciseness.

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

Completeness2/5

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

Given the complexity of generating embeddings (involving external API calls) and the lack of annotations and output schema, the description is incomplete. It doesn't address return values, error cases, or operational constraints, making it inadequate for safe and effective tool invocation by an agent.

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

Parameters3/5

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

The schema description coverage is 0%, so the description must compensate. It mentions 'array of texts', which aligns with the 'texts' parameter in the schema, adding some meaning. However, it doesn't explain details like text length limits, encoding, or handling of empty strings, so it only partially compensates for the lack of schema descriptions.

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

Purpose4/5

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

The description clearly states the action ('Generate') and resource ('OpenAI embeddings for an array of texts'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'index_documents' or 'vector_search', which might also involve embeddings, so it doesn't reach the highest score.

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 'index_documents' or 'vector_search'. It lacks context on use cases, 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.

Deploy Server