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mcp-server-lmstudio

mcp-server-lmstudio

by ibukichi-jp
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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ask_local_llm

Send prompts or coding questions directly to local LM Studio models. Use this tool to query your locally running LLM for answers while keeping data on your machine.

Instructions

Send a prompt or coding question to the local LLM running in LM Studio.

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": "ask_local_llm"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoOptional model ID. If omitted, the currently loaded model in LM Studio is used.
promptYesThe prompt / instruction / question to send to the local LLM
temperatureNoSampling temperature (default: 0.7)
system_promptNoOptional system instructions (e.g. 'You are an expert full-stack developer')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It doesn't state whether the local server must be running, what happens on failure, latency/timeout behavior, output format, or whether it's synchronous. 'Send a prompt' is all the agent gets.

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

Conciseness4/5

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

A single tight sentence with no waste. Appropriately sized, though its brevity is partly why other dimensions are thin.

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?

For a tool that performs inference against an external local server with no annotations and no output schema, the description leaves major gaps: no return value expectations, no error/timeout handling, no environment prerequisites. The agent cannot predict the response shape or failure modes.

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 baseline is 3. The description adds nothing about parameters (model defaulting, temperature, system_prompt) beyond what the schema already documents.

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?

Clear verb+resource: sends a prompt to the local LLM in LM Studio. It distinguishes itself reasonably from siblings (list_local_models, check_lm_studio_status) by being the only one about inference, 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 when-to-use guidance, no prerequisites (e.g., LM Studio must be running), and no mention of alternatives like check_lm_studio_status to verify availability first. The agent must infer all of this.

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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