Which integrations are available for this server?

Provides integration with a local Ollama server, enabling model discovery and chat completions for running agent tasks entirely offline. Allows using OpenAI models as optional frontier planners in hybrid mode, routing complex reasoning to the cloud while local models handle execution.

How do I use lmstudio-ollama-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@lmstudio-ollama-mcp add unit tests for src/utils/logger.ts" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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lmstudio-ollama-mcp

by fthsrbst

Overview Schema Related Servers Score Discussions

TypeScript

Local

lmstudio-ollama-mcp

npm install -g lmstudio-ollama-mcp
lmstudio-ollama-mcp doctor   # or: forge doctor
lmstudio-ollama-mcp "add unit tests for src/utils/logger.ts"

Local-first. Private. Free. No API keys required. Frontier models (GPT-4o, Claude 4) are optional — used only as planners while small local models do the work. Alias forge / forgecode keeps muscle memory.

Why lmstudio-ollama-mcp

	Claude Code / Codex	lmstudio-ollama-mcp
Runs on	Cloud API (paid, data leaves machine)	LM Studio · Ollama · llama.cpp (offline, private)
Cost	$ per token	$0 after model download
Sub-agents	Single-threaded or cloud parallelism	Hardware-aware local parallelism
Model choice	Vendor-locked	Any GGUF / OpenAI-compatible model
Hybrid mode	—	Frontier plans, local executes (optional)
Sandbox	Cloud container	Your filesystem, your rules
MCP	—	Ready: bridges local runtimes as MCP tools

Single sentence: lmstudio-ollama-mcp brings the Claude Code agentic loop — read → plan → edit → verify with tools — to your MacBook, with an intelligent router that sends trivial tasks to a local 7B and hard reasoning to a frontier model only when needed.

Related MCP server: Shared Workspace MCP

Demo

# 1 — Diagnose
lmstudio-ollama-mcp doctor
# Hardware: Apple M3 (8 cores / 16GB) • Recommended: 8 agents
# ● lmstudio (LM Studio) http://localhost:1234/v1  available
#   models: gemma-3-12b-qat, qwen3-27b-ud-iq2_s …

lmstudio-ollama-mcp models
# ● lmstudio  ▸ gemma-3-12b-qat 6.5GB Q4_0
#             ▸ qwen3-27b 7.8GB IQ2_S

# 2 — One-shot
lmstudio-ollama-mcp "refactor src/providers into a registry + add tests. keep public API stable"

# 3 — Parallel (auto-splits into sub-agents)
lmstudio-ollama-mcp --parallel 4 "implement auth module, write tests, and update docs"
# forge alias also works:
forge --parallel 4 "implement auth module, write tests, and update docs"

# 4 — Force a specific model
lmstudio-ollama-mcp --model ollama:qwen2.5-coder:14b "explain this repo's error handling"
lmstudio-ollama-mcp --provider lmstudio --model gemma-3-12b "fix the failing test in tests/tools.test.ts"

# 5 — Interactive
lmstudio-ollama-mcp
# lmstudio-ollama-mcp> add dark mode to docs/index.html

Quickstart

Prerequisites

Node.js >= 18
One of:
- LM Studio — Developer → Local Server → Start (port 1234)
- Ollama — ollama serve then ollama pull qwen2.5-coder:7b
- llama.cpp — ./llama-server -m model.gguf --port 8080

Install

npm install -g lmstudio-ollama-mcp
# aliases also available: forge, forgecode
# or one-off
npx lmstudio-ollama-mcp doctor

First run

git clone https://github.com/your-org/your-project && cd your-project
lmstudio-ollama-mcp init   # creates lmstudio-ollama-mcp.json (also reads forgecode.json for compat)
lmstudio-ollama-mcp doctor # verify providers + hardware
lmstudio-ollama-mcp "list the codebase structure and suggest 3 small improvements"

No API keys needed for local-only mode. For hybrid mode (frontier + local), set env:

export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...

Architecture

┌─────────────────────────────────────────────────────────┐
│  CLI  lmstudio-ollama-mcp "task"  •  doctor  •  models   │
│       aliases: forge, forgecode                         │
├─────────────────────────────────────────────────────────┤
│  Router  (strategy: auto | local-first | frontier-first)│
│  ├─ classify(prompt) → trivial | small | medium | large │
│  └─ thresholds.preferLocalFor: lint/format/test/search  │
├─────────────────────────────────────────────────────────┤
│  Orchestrator  (decompose → batch by deps → schedule)   │
│  ├─ Planner LLM decomposes goal → SubTasks[]            │
│  └─ Scheduler (hardware-aware p-limit, preserves order) │
├─────────────────────────────────────────────────────────┤
│  Agent Loop  (provider.chat ↔ tool executor)            │
│  ├─ Tools: read_file, write_file, edit_file, bash,      │
│  │        glob, grep, list_dir                           │
│  └─ Max 25 tool turns, exact-string edits               │
├─────────────────────────────────────────────────────────┤
│  Providers  (OpenAI-compatible)                         │
│  ├─ LM Studio  http://localhost:1234/v1  (+ fs scan)    │
│  ├─ Ollama     http://localhost:11434 (+ /api/tags)     │
│  ├─ llama.cpp  http://localhost:8080/v1                 │
│  └─ Frontier   OpenAI / Anthropic (optional)            │
├─────────────────────────────────────────────────────────┤
│  Hardware Detector  •  Scheduler                        │
│  cores × overcommit, free mem / perAgent → maxParallel │
│  Apple Silicon bonus, clamp 1..16                       │
└─────────────────────────────────────────────────────────┘

Data flow:

User prompt
  → Router.classify → pick provider+model (local for small, frontier for large)
  → If parallel & non-trivial: Orchestrator.decompose → 2-6 SubTasks
  → Scheduler.runAll(SubTasks) with maxParallel = f(cores, RAM)
  → Each SubTask → Agent(provider, model, ToolExecutor) → tool loop
  → Synthesis agent merges results
  → Final summary

Providers

Provider	Default URL	Discovery	Notes
LM Studio	`http://localhost:1234/v1`	`/v1/models` + `~/.lmstudio/models/*/.gguf` scan	Supports `reasoning_content` (Gemma)
Ollama	`http://localhost:11434`	`/api/tags` native, fallback `/v1/models`	`ollama pull <model>` required
llama.cpp	`http://localhost:8080`	`/health` + `/v1/models`	Any GGUF via `llama-server`
OpenAI	`https://api.openai.com/v1`	API	Set `OPENAI_API_KEY`
Anthropic	`https://api.anthropic.com`	API	Set `ANTHROPIC_API_KEY`

All providers speak OpenAI-compatible Chat Completions with tools (function calling). Normalizes reasoning_content (Qwen/Gemma) automatically.

Adding a custom endpoint

// lmstudio-ollama-mcp.json
{
  "providers": {
    "my-local": { "type": "openai", "baseUrl": "http://192.168.1.10:1234/v1", "enabled": true }
  }
}

Parallel Sub-Agents

Splits complex goals into 2–6 independent sub-tasks via a planner LLM (frontier if available, otherwise local). Execution is bounded by hardware:

// hardware/detector.ts — recommendParallelism()
cpuLimit = floor(cores * overcommit) - 1
memLimit = floor((totalGb*1024 - 2048) / perAgentMb)
maxParallel = min(cpuLimit, memLimit) + appleSiliconBonus
// clamp: 1..8 default, up to 16 on 64GB machines

lmstudio-ollama-mcp --parallel 8 "migrate codebase from Jest to Vitest"
# Decomposed:
#  t1 Explore & plan  →  search (routed to local 7B)
#  t2 Implement       →  code   (routed to local or frontier)
#  t3 Verify          →  test   (routed to local)
# Runner: Scheduler.runAll with p-limit = 8

Tasks with dependsOn are batched topologically — batch N only starts after N-1 completes.

Local-model friendly: trivial tasks (lint, format, summarize, explain) are always routed locally regardless of strategy.

Configuration

Config resolution: DEFAULT < ~/.lmstudio-ollama-mcp/config.json < ./lmstudio-ollama-mcp.json < env vars.
Legacy ~/.forgecode/config.json and forgecode.json / forge.json are still read for backward compat (new path takes precedence).

lmstudio-ollama-mcp config --show   # resolved JSON
lmstudio-ollama-mcp config --path   # file locations
lmstudio-ollama-mcp init            # scaffold lmstudio-ollama-mcp.json

`lmstudio-ollama-mcp.json` reference

{
  "version": 1,
  "providers": {
    "lmstudio": { "type": "lmstudio", "baseUrl": "http://localhost:1234/v1", "enabled": true },
    "ollama":   { "type": "ollama",   "baseUrl": "http://localhost:11434",      "enabled": true },
    "llamacpp": { "type": "llamacpp", "baseUrl": "http://localhost:8080",       "enabled": true },
    "openai":   { "type": "openai",   "baseUrl": "https://api.openai.com/v1",   "apiKey": "sk-..." }
  },
  "router": {
    "strategy": "auto", // auto | local-first | frontier-first | local-only
    "frontierProvider": "openai",
    "frontierModel": "gpt-4o-mini",
    "thresholds": {
      "smallTaskMaxTokens": 2000,
      "preferLocalFor": ["lint","format","test","search","summarize","explain"]
    }
  },
  "hardware": {
    "maxParallelAgents": 4,      // auto if omitted
    "maxMemoryPerAgentMb": 1200,
    "cpuOvercommit": 1
  },
  "permissions": {
    "allowBash": true,
    "allowWriteOutsideWorkspace": false,
    "allowNetwork": true
  }
}

Strategies:

auto — trivial/small → local, medium/large → frontier if available else local. (recommended)
local-first — only medium/large go to frontier.
local-only — never call frontier (air-gapped).
frontier-first — always prefer frontier.

Tools

Agents have 7 tools — the same surface as Claude Code, sandboxed to the workspace:

Tool	Description
`read_file`	Read a file (2 MB limit, else use grep)
`write_file`	Create/overwrite a file (mkdir -p auto)
`edit_file`	Exact-string replacement (must match once)
`bash`	Run a command (`timeout 30s`, 5 MB buffer)
`glob`	`fast-glob` search
`grep`	Regex search (skips `node_modules/dist/.git`)
`list_dir`	Directory listing

Safety: path escape blocked unless permissions.allowWriteOutsideWorkspace=true; dangerous commands (rm -rf /) rejected; large outputs truncated (30k).

Comparison: When to use which model

Task	Why local wins	Example
Lint / format / grep	0.2s vs 2s RTT	`lmstudio-ollama-mcp "format src/*/.ts with prettier"`
Explain / summarize	Private codebase stays local	`lmstudio-ollama-mcp "explain how auth works"`
Small edits	No queue, no cost	`lmstudio-ollama-mcp "add zod validation to src/config/schema.ts"`
Large refactor	Frontier plans, locals execute in parallel	`lmstudio-ollama-mcp "migrate to ESM"`
Hard reasoning	70B / frontier needed	`lmstudio-ollama-mcp --model openai:gpt-4o "design CRDT sync"`

Development

npm install
npm run build        # tsc
npm test             # vitest
npm run dev -- doctor

Project map:

src/
  cli/            commander CLI + commands (doctor, models, config, init)
  config/         Zod schema + layered store (global ↔ project)
  hardware/       detector (cores/RAM/GPU) + p-limit scheduler
  providers/      base + openai-compatible + lmstudio/ollama/llamacpp + registry + router
  core/           Agent (tool loop) + Orchestrator (decompose + parallel)
  tools/          definitions + executor (fs/glob/grep/bash)
  utils/          logger, format
tests/            vitest suites (hardware, tools, router, config, providers)
docs/             GitHub Pages landing (WizardZ-inspired, lime/black)

Roadmap

Streaming output (--stream)
MCP (Model Context Protocol) server — expose local models as MCP tools for other agents
Persistent memory (.lmstudio-ollama-mcp/memory.md)
lmstudio-ollama-mcp plan — dry-run decomposition without execution
Vision models (Gemma 12B multimodal) for screenshot-driven UI work
hooks — pre/post tool hooks
Windows / Linux GPU (CUDA/Vulkan) scheduler hints

Contributing

PRs welcome. Keep the core principles: local-first, minimal deps, hardware-aware, no AI slop.

npm run build && npm test

Keywords

lm-studio lmstudio ollama llama.cpp local-llm local-first coding-agent autonomous-agent claude-code codex sub-agents parallel-agents mcp model-context-protocol hardware-aware openai-compatible gguf agentic dev-tools ai-coding on-device-ai privacy

License

MIT — see LICENSE.

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lmstudio-ollama-mcp

Why lmstudio-ollama-mcp

Demo

Quickstart

Prerequisites

Install

First run

Architecture

Providers

Adding a custom endpoint

Parallel Sub-Agents

Configuration

lmstudio-ollama-mcp.json reference

Tools

Comparison: When to use which model

Development

Roadmap

Contributing

Keywords

License

Maintenance

Resources

Looking for Admin?

Related MCP Servers

Serena

Shared Workspace MCP

Hecatoncheire MCP

christina

Related MCP Connectors

Latest Blog Posts

MCP directory API

`lmstudio-ollama-mcp.json` reference