Ollama-Omega

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OLLAMA-OMEGA

MCP server — Ollama bridge for any IDE. Sovereign compute. No cloud dependency.

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!Status !Version !Stack !License !Tests

<a href="https://glama.ai/mcp/servers/VrtxOmega/Ollama-Omega"><img width="380" height="200" src="https://glama.ai/mcp/servers/VrtxOmega/Ollama-Omega/badges/card.svg" alt="Ollama-Omega MCP server" /></a>


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Ecosystem Canon

Ollama-Omega is the compute interface layer of the VERITAS & Sovereign Ecosystem (Omega Universe). It surfaces every locally installed Ollama model — and any cloud-hosted model accessible through an Ollama daemon — as a structured MCP tool set inside any MCP-compatible IDE or agent runtime.

Within the Omega Universe, governance flows downward from omega-brain-mcp (the VERITAS gate and approval pipeline) to Ollama-Omega (the inference transport). Ollama-Omega is the final execution node: it issues the prompt, receives model output, and returns a validated, schema-typed response. No inference executes before the upstream gate approves the request.

Ollama-Omega does not perform memory, authentication, persistence, or policy enforcement. Those responsibilities belong to the operators above it in the stack. This node does one thing: connect IDE to Ollama, reliably and without information loss.

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Overview

What it is:

• A single-file MCP server (ollama_mcp_server.py) that bridges Ollama into any MCP-compatible client
• Six validated tools covering health, model listing, chat, generation, model inspection, and model pull
• Compatible with Claude Desktop, VS Code + Continue, Cursor, Antigravity IDE, and any other client that speaks MCP over stdio

What it is not:

• A full AI platform, memory layer, or policy engine
• A replacement for the Ollama daemon — it wraps the daemon's HTTP API over MCP stdio transport
• A cloud service — all inference is local or routed through your own Ollama daemon

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Features

| Feature | Detail | |---------|--------| | 6 MCP tools | Health check, list models, chat, generate, show model info, pull model | | Stdio transport | JSON-RPC 2.0 over stdin/stdout — no network ports opened by this server | | Typed output schemas | Every tool carries a full outputSchema for structured agent consumption | | SSRF mitigation | follow_redirects=False on the singleton httpx client | | Input validation | _validate_required() gate before any HTTP call; no uncaught KeyError | | Safe JSON handling | _safe_json() wrapper — no crash on malformed Ollama responses | | Error sanitization | _error() helper — no stack traces, no internals exposed to the client | | Cloud model support | Any model accessible on your Ollama daemon is available — no config change required | | Docker-ready | Dockerfile included for containerized deployment |

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Architecture

  IDE / MCP Client
  (Claude Desktop, VS Code + Continue, Cursor, Antigravity, ...)
         |
         |  stdio  JSON-RPC 2.0
         v
  +-----------------------------+
  |    ollama_mcp_server.py     |
  |  Validator  |   Dispatch    |
  |  Singleton httpx AsyncClient|
  +-----------------------------+
         |
         |  HTTP  (default: http://localhost:11434)
         v
  +-----------------------------+
  |       Ollama Daemon         |
  |  Local models  (GPU / CPU)  |
  |  Cloud proxy models         |
  +-----------------------------+
         |
         v
  Local model store
  (~/.ollama/models)

The server process lives for the lifetime of the IDE session. One httpx AsyncClient handles all upstream Ollama HTTP traffic. The MCP client never communicates with Ollama directly.

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Quickstart

Prerequisites

• Python 3.11 or later
• Ollama daemon installed and running

Install Ollama

| Platform | Method | |----------|--------| | Windows | Download the installer from ollama.com/download/windows and run it. Ollama starts automatically as a system tray service. | | macOS | Download from ollama.com/download/mac, or via Homebrew: brew install ollama && ollama serve | | Linux | curl -fsSL https://ollama.com/install.sh \| sh — starts the daemon via systemd on supported distributions |

Verify the daemon is reachable before proceeding:

curl http://localhost:11434
# Expected response: Ollama is running

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Install Ollama-Omega

Option A — pip (simplest):

pip install mcp httpx

Then download the server file:

# macOS / Linux
curl -O https://raw.githubusercontent.com/VrtxOmega/Ollama-Omega/master/ollama_mcp_server.py

# Windows (PowerShell)
Invoke-WebRequest -Uri https://raw.githubusercontent.com/VrtxOmega/Ollama-Omega/master/ollama_mcp_server.py -OutFile ollama_mcp_server.py

Option B — clone the repository (recommended for local development):

git clone https://github.com/VrtxOmega/Ollama-Omega.git
cd Ollama-Omega
pip install mcp httpx

Option C — uv (virtual-env isolation, recommended for production):

git clone https://github.com/VrtxOmega/Ollama-Omega.git
cd Ollama-Omega
uv sync

Option D — Docker:

git clone https://github.com/VrtxOmega/Ollama-Omega.git
cd Ollama-Omega
docker build -t ollama-omega .
# Run with stdio transport for IDE integration:
docker run -i --rm -e OLLAMA_HOST=http://host.docker.internal:11434 ollama-omega

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Pull a model

ollama pull llama3.2:3b

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Configure your MCP client

Edit the configuration file for your IDE and add the ollama server block. Replace /path/to/Ollama-Omega with the actual path to your clone (or the directory containing ollama_mcp_server.py).

Claude Desktop

Config file locations:

| Platform | Path | |----------|------| | Windows | %APPDATA%\Claude\claude_desktop_config.json | | macOS / Linux | ~/.config/Claude/claude_desktop_config.json |

{
  "mcpServers": {
    "ollama": {
      "command": "python",
      "args": ["/path/to/Ollama-Omega/ollama_mcp_server.py"],
      "env": {
        "PYTHONUTF8": "1",
        "OLLAMA_HOST": "http://localhost:11434",
        "OLLAMA_TIMEOUT": "300"
      }
    }
  }
}

With uv (virtual-env isolation):

{
  "mcpServers": {
    "ollama": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/Ollama-Omega",
        "run",
        "python",
        "ollama_mcp_server.py"
      ],
      "env": {
        "PYTHONUTF8": "1",
        "OLLAMA_HOST": "http://localhost:11434",
        "OLLAMA_TIMEOUT": "300"
      }
    }
  }
}

VS Code + Continue / Cursor

Most MCP-compatible VS Code extensions follow the same JSON structure under their own config key. Substitute the command and args block from the Claude Desktop example above. Consult your extension's documentation for the exact config file path.

Antigravity IDE

Config file: ~/.gemini/antigravity/mcp_config.json

{
  "mcpServers": {
    "ollama": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/Ollama-Omega",
        "run",
        "python",
        "ollama_mcp_server.py"
      ],
      "env": {
        "PYTHONUTF8": "1",
        "OLLAMA_HOST": "http://localhost:11434"
      }
    }
  }
}

Restart your IDE after saving the configuration file. Verify connectivity by calling the ollama_health tool from your IDE.

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Configuration

| Variable | Default | Description | |----------|---------|-------------| | OLLAMA_HOST | http://localhost:11434 | Base URL of the Ollama daemon. Override to point at a remote or containerized daemon. | | OLLAMA_TIMEOUT | 300 | HTTP request timeout in seconds. Increase for large model pulls or slow cloud inference. | | PYTHONUTF8 | _(unset)_ | Set to 1 on Windows to prevent Unicode encoding errors in stdio transport. |

Cloud-hosted models exposed by your Ollama daemon (e.g., qwen3.5:397b-cloud via API proxy) are accessible through the same 6 tools with no configuration change. Authenticate first with ollama login.

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Troubleshooting

Ollama daemon not running

# Start the daemon
ollama serve

# Verify
curl http://localhost:11434

If OLLAMA_HOST is set to a non-default value, confirm the URL and port match the daemon's bind address.

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Port conflict — daemon fails to start

Ollama binds to port 11434 by default. If that port is occupied:

# macOS / Linux — find the occupying process
lsof -i :11434

# Windows (PowerShell)
netstat -ano | findstr :11434

Set OLLAMA_HOST to an alternate port once you have reconfigured the daemon.

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Model not found / HTTP 404

The referenced model has not been pulled. Pull it first:

ollama pull <model-name>

# Cloud-hosted models require authentication:
ollama login
ollama pull qwen3.5:397b-cloud

Alternatively, call ollama_pull_model from your IDE once the server is connected.

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Tools do not appear in the IDE

1. Confirm the command path resolves to a working Python 3.11+ interpreter.
2. Confirm mcp and httpx are installed in that interpreter's environment.
3. Restart the IDE — MCP servers are discovered at startup, not while running.
4. Check IDE logs for JSON-RPC handshake errors.

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Windows: UnicodeEncodeError or garbled output

Set PYTHONUTF8=1 in the server's env block. This is already shown in the configuration examples above.

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Docker: cannot reach localhost:11434

Docker containers run in an isolated network namespace. Replace localhost with host.docker.internal:

docker run -i --rm -e OLLAMA_HOST=http://host.docker.internal:11434 ollama-omega

On Linux hosts, --network=host may be required instead.

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Request timed out after 300s

Cold inference on large models (70B+) or cloud-proxied models can exceed the default timeout. Increase it in your MCP client config:

"env": { "OLLAMA_TIMEOUT": "600" }

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Security and Sovereignty

Ollama-Omega runs exclusively on localhost by default, communicating with the Ollama daemon over the loopback interface. No data leaves the machine unless your Ollama daemon is configured to proxy to a cloud endpoint.

Hardening applied to this server:

| Control | Implementation | |---------|----------------| | SSRF prevention | follow_redirects=False on the httpx singleton | | Input sanitization | Required-argument validation before any outbound HTTP call | | Error sanitization | Internal errors are never forwarded to the MCP client | | Non-root Docker | Container process runs as a dedicated ollama user |

Limitations and out-of-scope items:

• Authentication between the MCP client and this server is not implemented. MCP stdio transport is inherently scoped to the local process boundary.
• This server does not validate the content of prompts or model outputs. Content policy enforcement is the responsibility of the upstream operator (see omega-brain-mcp).
• Network isolation, host-level security, and key management are outside the scope of this component.

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Omega Universe

Ollama-Omega is one node in the VERITAS & Sovereign Ecosystem. Cross-references:

| Repository | Role in the stack | |------------|-------------------| | omega-brain-mcp | VERITAS gate + cryptographic audit ledger + Cortex approval pipeline. The governance layer above Ollama-Omega. | | veritas-vault | Long-term retention substrate. Stores artifacts, attestations, and approved outputs. | | Aegis | Security enforcement layer. Threat surface scanning and sovereign boundary enforcement. | | drift | Drift detection and configuration integrity monitoring across Omega operators. | | SovereignMedia | Media processing and content pipeline within the Omega framework. | | sovereign-arcade | Operator sandbox and demonstration environment for Omega Universe components. |

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🌐 VERITAS Omega Ecosystem

This project is part of the VERITAS Omega Universe — a sovereign AI infrastructure stack.

• VERITAS-Omega-CODE — Deterministic verification spec (10-gate pipeline)
• omega-brain-mcp — Governance MCP server (Triple-A rated on Glama)
• Gravity-Omega — Desktop AI operator platform
• Ollama-Omega — Ollama MCP bridge for any IDE
• OmegaWallet — Desktop Ethereum wallet (renderer-cannot-sign)
• veritas-vault — Local-first AI knowledge engine
• sovereign-arcade — 8-game arcade with VERITAS design system
• SSWP — Deterministic build attestation protocol

License

MIT — see LICENSE.

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<div align="center"> _{Built by <a href="https://github.com/VrtxOmega">RJ Lopez</a> | VERITAS Omega Framework} </div>