# Local-AI MCP servers: Ollama & vLLM model management and local LLM access for Claude, ChatGPT and other MCP clients [![CI](https://github.com/setheerwagen/local-ai-mcp-servers/actions/workflows/ci.yml/badge.svg)](https://github.com/setheerwagen/local-ai-mcp-servers/actions/workflows/ci.yml) [![PyPI mcp-modelmanager](https://img.shields.io/pypi/v/mcp-modelmanager)](https://pypi.org/project/mcp-modelmanager/) [![PyPI mcp-ollama-vllm](https://img.shields.io/pypi/v/mcp-ollama-vllm)](https://pypi.org/project/mcp-ollama-vllm/) [![Downloads mcp-modelmanager](https://img.shields.io/pypi/dm/mcp-modelmanager?label=downloads%20mcp-modelmanager)](https://pypistats.org/packages/mcp-modelmanager) [![Downloads mcp-ollama-vllm](https://img.shields.io/pypi/dm/mcp-ollama-vllm?label=downloads%20mcp-ollama-vllm)](https://pypistats.org/packages/mcp-ollama-vllm) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/setheerwagen/local-ai-mcp-servers/blob/main/LICENSE) ![Python](https://img.shields.io/badge/python-3.10%2B-blue) [![Listed on mcpservers.org](https://mcpservers.org/badge.svg)](https://mcpservers.org/servers/setheerwagen/local-ai-mcp-servers) [![Local AI MCP Servers MCP server – quality and maintenance score on Glama](https://glama.ai/mcp/servers/setheerwagen/local-ai-mcp-servers/badges/score.svg)](https://glama.ai/mcp/servers/setheerwagen/local-ai-mcp-servers) [![Local AI MCP Servers MCP server – quality and maintenance score on Glama](https://glama.ai/mcp/servers/setheerwagen/local-ai-mcp-servers/badges/card.svg)](https://glama.ai/mcp/servers/setheerwagen/local-ai-mcp-servers) [![Ollama / vLLM Bridge MCP server](https://mcprush.com/badge/ollama-vllm-bridge-mcp/card.svg)](https://mcprush.com/to/ollama-vllm-bridge-mcp?ref=badge) Two self-hostable [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) servers for running and steering your own local language models: manage an Ollama/vLLM model machine, and query a local LLM (Ollama, vLLM or an OpenAI-compatible backend) for language work with schema-validated JSON output. Self-hosted and privacy-first: the models and their machine stay on your own hardware, and neither server process reaches out to a public address on its own. Version 1.0.0, single-operator tool, actively used by the author. Both servers speak the Model Context Protocol (MCP), so an AI tool such as Claude or ChatGPT can use them. Each server's own traffic goes only to your own computer or your local network; a start-up guard rejects public addresses. Installing the two direct dependencies needs the internet once, at setup time. Fetching a model is a separate matter: in `mcp-modelmanager`, calling `fetch_model` is a deliberate, tool-triggered connection from your model machine to a public model registry, at whatever point you invoke it, not something limited to setup time. The default `source="ollama"` makes the machine's own Ollama daemon pull from `registry.ollama.ai` for plain model names, or from whatever other registry a prefixed name such as `hf.co/user/repo` points at; `source="huggingface"` instead reaches Hugging Face directly. `switch_service` never reaches either registry itself; it refuses and asks for `fetch_model` first if the model is not already on the machine. Each server stands on its own: pick one, or run both. A caveat: the AI tool you *drive* these servers with (Claude, ChatGPT, etc.) is itself a hosted service in the usual case. The instructions you give it and the tool inputs and results it handles pass through that provider, exactly as any other MCP call would. "Local" here means the models, the model machine and its data; it is not a claim that nothing you type ever reaches a third party. Drive them with a local model if that matters to you. ## The two servers ### `mcp-modelmanager` -> [mcp-modelmanager/](mcp-modelmanager/) Manage your own model machine through an AI tool: read state (GPU memory, disk, running service, containers), pull and remove models, switch the vLLM inference service to a different model, build custom Ollama variants and run LoRA training. There is deliberately no tool for free remote control: every operation is a fixed template and every parameter first passes an allowlist, checked independently on both sides. This server ships both halves: the client/server code the AI tool talks to, and the machine-side scripts, wrapper and network-fence templates that run on and lock down the model machine. ### `mcp-ollama-vllm` -> [mcp-ollama-vllm/](mcp-ollama-vllm/) Call a local model directly from an AI session for the language work: writing, summarizing, classifying, extracting structured data. Its distinctive feature is schema-validated JSON output, checked by its own dependency-free validator and retried automatically on a violation, the same way whether the configured backend is Ollama or vLLM. It is a model call, not an agent: no tools, no file access, no shell. The two are complementary. `mcp-ollama-vllm` *asks* a model (read-only, no management rights); `mcp-modelmanager` *manages* the machine. Keeping them separate means a query call never accidentally holds management rights. ## Repository layout ``` . |-- README.md this file |-- CHANGELOG.md version history (Keep a Changelog style) |-- CONTRIBUTING.md how to contribute |-- LICENSE MIT, covers the repo as a whole |-- .gitignore |-- .github/ | `-- workflows/ | `-- ci.yml lockfile check, locked-tree tests, shell lint, advisory pip-audit |-- mcp-modelmanager/ manage your own model machine (server + machine side) | |-- README.md server and client guide (start here) | |-- SETUP.md client-side step-by-step setup | |-- AGENT-SETUP.md setup procedure written for an AI agent | |-- MACHINE.md machine-side overview (start here for the machine) | |-- SETUP-MACHINE.md gapless machine provisioning | |-- SECURITY.md the three-layer security model | |-- LICENSE | |-- .gitignore SSH-key and build patterns (kept from the server) | |-- requirements.txt | |-- pyproject.toml | |-- uv.lock full resolved dependency tree, pinned | |-- src/mcp_modelmanager/ server package (server code and shared modules) | |-- setup/ machine-side setup scripts | |-- vm_side/ machine-side wrapper and job runners | `-- security/ network-fence templates (sshd, firewall, WireGuard) `-- mcp-ollama-vllm/ call local models with schema-checked JSON |-- README.md server guide (start here) |-- AGENT-SETUP.md setup procedure written for an AI agent |-- SECURITY.md what the bridge does and does not protect |-- LICENSE |-- requirements.txt |-- pyproject.toml |-- uv.lock full resolved dependency tree, pinned `-- src/mcp_ollama_vllm/ server package (server code) ``` ## Quickstart Each server has its own guide; start with the README in its folder. - `mcp-modelmanager`: read [mcp-modelmanager/README.md](mcp-modelmanager/README.md) and follow [mcp-modelmanager/SETUP.md](mcp-modelmanager/SETUP.md) for the client side. For the model machine itself, start from [mcp-modelmanager/MACHINE.md](mcp-modelmanager/MACHINE.md). - `mcp-ollama-vllm`: read [mcp-ollama-vllm/README.md](mcp-ollama-vllm/README.md); it has no separate SETUP.md (the README covers setup, there is no machine side and no management rights to document separately), and its SECURITY.md states the boundaries of the bridge. `mcp-modelmanager` needs Python 3.11 or newer, `mcp-ollama-vllm` 3.10 or newer; each has only two **direct**, pinned dependencies (`mcp` and `httpx`); `mcp` itself pulls in a number of further packages transitively (`anyio`, `pydantic`, `starlette`, `uvicorn`, `jsonschema`, `pyjwt` among them). The full resolved tree, direct and transitive, is pinned per server in its `uv.lock`; CI checks with `uv lock --check` that the lock still matches `pyproject.toml` and runs the tests against the exact locked tree, so a drifted lock fails CI. No account, no paid service, no cloud is required. ## Installation / Usage Install whichever server(s) you need from PyPI: ```bash pip install mcp-modelmanager pip install mcp-ollama-vllm ``` Each ships a console entry point (`mcp-modelmanager` / `mcp-ollama-vllm`) after install, so an MCP client can point `command` straight at it, for example: ```json { "mcpServers": { "modelmanager": { "command": "/path/to/.venv/bin/mcp-modelmanager", "env": { "MM_ACCESS": "direct", "MM_VM_HOST": "", "MM_VM_USER": "", "MM_CONTAINER_ROOT": "/srv/models" } }, "local-models": { "command": "/path/to/.venv/bin/mcp-ollama-vllm", "env": { "LOCAL_BACKEND": "ollama", "LOCAL_HOST": "http://localhost:11434" } } } } ``` The required environment variables differ per server (`mcp-modelmanager` needs the three `MM_*` fields above to start at all; `mcp-ollama-vllm` runs with defaults for a local Ollama). This is only the shape; do not copy the values verbatim. For the full, current install/registration steps, including the checkout-based (non-PyPI) path and running from source, see each server's own README: [mcp-modelmanager/README.md](mcp-modelmanager/README.md#installation) and [mcp-ollama-vllm/README.md](mcp-ollama-vllm/README.md#installation). ## Security and scope - **Local and self-hosted.** Both server processes run on your own hardware and talk only to your own computer or your local/private network. Public addresses are rejected at start-up, and names are resolved so the guard cannot be bypassed via DNS. This governs the *servers'* own traffic; the AI tool that drives them is a separate service (see the caveat above), and so is a model download you explicitly trigger through `mcp-modelmanager` (`fetch_model`): with the default `source="ollama"` it makes the machine's own Ollama daemon pull from `registry.ollama.ai` (or from the registry a prefixed model name such as `hf.co/user/repo` points at), and with `source="huggingface"` it reaches Hugging Face directly, both from the model machine at the time you ask for it. `switch_service` does not reach either registry; it refuses a model that is not already on the machine and points you at `fetch_model` instead. - **No credentials in this repository.** No keys, tokens or passwords are stored here; SSH keys for `mcp-modelmanager` live outside the repo in your `~/.ssh`, and the `.gitignore` additionally excludes key and `.env` patterns. - **Placeholders, not real values.** Every machine-specific value in the docs and templates is a `` or an environment variable you fill in on your own machine at setup time. - **Each server has its own `SECURITY.md`** ([mcp-modelmanager](mcp-modelmanager/SECURITY.md), [mcp-ollama-vllm](mcp-ollama-vllm/SECURITY.md)) that goes into the actual trust boundaries and layers, and states plainly what remains unaddressed. ## License MIT for both servers. See the `LICENSE` file in each server folder. Copyright (c) 2026 Siegfried Emil Timothy Heerwagen.