McPlex

MCP server that bridges local Ollama models to Claude Code and other MCP clients -- text generation, embeddings, vision, and vector memory, all running locally.

Why I Built This

Claude Code is powerful but cloud-only. Local models via Ollama are private and free but disconnected from MCP tooling. I needed a bridge: expose local models as MCP tools so Claude Code can delegate tasks to local inference (summarization, embedding, image analysis) without API costs or data leaving my machine. Any MCP-compatible client (Claude Code, Cursor, etc.) gets access with zero custom integration.

What It Does

• 9 MCP tools -- generate, chat, embed, list_models, analyze_image, ocr_image, memory_store, memory_search, memory_list_collections
• Zero cloud dependency -- all inference runs locally via Ollama; no API keys needed
• ChromaDB vector memory -- store and semantically search text with persistent local storage
• Vision and OCR -- analyze images and extract text using local vision models (LLaVA)
• Drop-in MCP config -- add 3 lines to Claude Code's MCP config and local models are available immediately

Key Technical Decisions

• MCP protocol over custom API -- standard protocol means any MCP client works without custom integration code. When a new MCP client launches, McPlex works with it automatically.
• Ollama over vLLM -- simpler setup, built-in model management (ollama pull), runs on consumer hardware. vLLM is faster at scale but requires manual model configuration and more VRAM.
• Lazy ChromaDB loading -- memory tools are optional. Core text/vision tools work without ChromaDB installed. pip install mcplex[memory] adds vector storage only when needed.
• Async HTTP via httpx -- non-blocking Ollama API calls. Multiple tools can query different models concurrently without blocking the MCP event loop.

Quick Start

pip install mcplex              # Core (text + vision)
pip install mcplex[memory]      # With ChromaDB vector memory

# Requires Ollama running locally
ollama pull qwen3:8b            # Pull a model

Add to Claude Code MCP config:

{
  "mcpServers": {
    "mcplex": {
      "command": "mcplex",
      "args": []
    }
  }
}

Then ask Claude Code: "Use the generate tool to summarize this file with qwen3:8b"

Configuration via environment variables:

MCPLEX_OLLAMA_URL=http://localhost:11434
MCPLEX_DEFAULT_MODEL=qwen3:8b
MCPLEX_CHROMA_PATH=./mcplex_data/chroma

Lessons Learned

MCP tool schema design matters more than implementation quality. Overly flexible schemas (e.g., a single query tool that accepts model, prompt, temperature, max_tokens, format, and system prompt) confuse LLM clients -- they don't know which parameters to set. Specific, well-documented tool signatures with sensible defaults (generate takes a prompt and optional model) produce much better tool-calling accuracy. I went through three schema iterations before landing on the current 9-tool design, and each simplification improved Claude Code's ability to use the tools correctly.

Tests

pip install -e ".[memory,dev]"
pytest tests/ -v    # 24 tests

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MIT License. See LICENSE.