LM Studio MCP Connector & Daemon

lmstudio_chat

Send prompts to a local LM Studio model and retrieve generated completions; if local instances are offline, returns fallback guidance to use remote LLMs.

Instructions

Send a prompt or instruction to the local LM Studio model and get the generated completion. If all local instances are offline, returns a directive instructing the agent to fallback to remote LLMs.

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": {
      "prompt": "<prompt>"
    },
    "name": "lmstudio_chat"
  }
}

Input Schema

Name	Required	Description
`model`	No	Optional model identifier (defaults to currently loaded model in LM Studio)
`prompt`	Yes	The prompt or instruction to send to the local model
`max_tokens`	No	Maximum tokens to generate in response (default: 2048)
`temperature`	No	Sampling temperature between 0.0 and 2.0 (default: 0.7)
`system_prompt`	No	Optional system instructions guiding the model behavior

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Oct 23, 2026

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It usefully discloses that the tool returns a fallback directive when all local instances are offline, but it does not clarify permissions, whether prompts are stored, response streaming, or other operational behaviors.

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 two focused sentences with the primary purpose front-loaded and a useful fallback condition second. There is no redundant or filler text.

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

Completeness3/5

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

The description is adequate for a chat-completion tool with fully documented parameters, but with no output schema it does not fully describe the return shape beyond 'generated completion' and the offline fallback directive. Error handling and model-selection behavior are left implicit.

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 description coverage is 100%, so the schema already documents prompt, model, max_tokens, temperature, and system_prompt. The description mentions sending a prompt and getting a completion but adds no parameter-level meaning beyond the schema.

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 states a specific action and resource: send a prompt or instruction to the local LM Studio model and receive a generated completion. This clearly separates it from sibling tools like list_models, embeddings, health, and pool_status, though it does not explicitly name those alternatives.

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 gives no explicit guidance on when to use this tool versus alternatives such as lmstudio_embeddings. It mentions fallback behavior when local instances are offline, but that is a behavioral note rather than usage guidance for choosing this tool.

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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