LM Studio MCP Connector & Daemon

lmstudio_embeddings

Computes vector embeddings for input text using a local LM Studio embedding model, enabling semantic search, clustering, and similarity comparisons.

Instructions

Generate vector embeddings for input text using the local LM Studio embedding model.

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": "lmstudio_embeddings"
  }
}

Input Schema

Name	Required	Description	Default
`input`	Yes	The text to compute embeddings for
`model`	No	Optional embedding model identifier (defaults to loaded embedding model)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Oct 23, 2026

TDQS

B3.2/5.0

Behavior3/5

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

With no annotations, the description carries the full burden; it does disclose that inference is local and that the model defaults to the loaded embedding model, which is real context. It omits failure behavior when no model is loaded, whether inputs can be batched, and the output vector shape — meaningful gaps for an unannotated tool.

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?

A single front-loaded sentence with no filler; verb and resource appear immediately.

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?

No output schema and no annotations, yet the description never states the return value (vector format/dimensions) or the model-must-be-loaded precondition. For a compute tool whose result shape is entirely undocumented, this leaves an agent under-informed.

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?

Schema coverage is 100%, so both parameters are already documented, including the model default. The description adds nothing beyond 'input text' and the fact that the model is the local LM Studio one, so the baseline 3 applies.

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?

Specific verb (generate) plus resource (vector embeddings) over input text, naming the LM Studio embedding model. It is clearly distinct from the chat/completions siblings by operation type, though it never names them explicitly.

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?

No guidance on when to prefer embeddings over lmstudio_chat, no prerequisites (e.g., a model must be loaded), and no mention of alternatives such as lmstudio_list_models to verify availability. The use case is left entirely to inference.

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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