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offline-mcp

offline-mcp

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github.com/gabrielmahiaAI & Agents0Updated 3d ago
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About offline-mcp

offline-mcp is an MCP server for local AI inference and offline deployment guidance using Ollama and open-weight models. It is aimed at deployments with limited connectivity, with tools for model status, inference, hardware-aware guidance, and degraded operation.

Key features

  1. 01Check local Ollama status and models
  2. 02Run prompts through local Ollama models
  3. 03Browse recommended open-weight models
  4. 04Guides for degraded offline operation
  5. 05Model directory with Africa language support
  6. 06Deployment guides for several device types

Use cases

  1. 01Running inference without internet access
  2. 02Checking locally available Ollama models
  3. 03Selecting open-weight models for local tasks
  4. 04Planning Raspberry Pi or laptop deployment

MCP support

Exposes six tools to MCP clients for local Ollama status and inference, model discovery, offline-mode guidance, and local deployment information.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "gabrielmahia-offline": {
      "command": "uvx",
      "args": [
        "offline-mcp"
      ]
    }
  }
}
Run commanduvx offline-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypioffline-mcp0.1.10

Tools

6

Saved tool list · updated 1w ago

This tool takes no parameters.

Details

From the project README

offline-mcp

offline-mcp Glama score smithery badge

Install: pip install offline-mcp · Use with any MCP client.

Local AI inference infrastructure — Ollama wrapper, open weights directory, degraded-mode guide for East Africa.

PyPI Thesis Layer

Why: Never assume OpenAI survives, Anthropic stays accessible, or export controls disappear. This matters more in Africa than anywhere else.

1st world equivalent: Ollama, LLaMA, Mistral local deployment

Why This Exists: Data Sovereignty

"If you take the deal, you're going to be exploited. If you don't take it, you're going to die."
— Frank Ssekamwa, Ugandan digital rights expert

Across the Global South, AI and health data from communities is being extracted, processed abroad, and used to build models whose value flows away from the communities that generated it.

offline-mcp is the sovereignty floor of the East Africa coordination stack.

When this runs on a Raspberry Pi 4 with a 50W solar panel and a 256GB SD card:

  • Health data stays in the clinic. Health guidance comes from local models.
  • Land records stay in the land office. Queries don't touch foreign servers.
  • Civic data stays in the county. AI assistance runs without internet.

No API key. No cloud dependency. No data leaving the community.

The models available via offline-mcp (Llama 3.2, Qwen 2.5) run entirely on device. Community data used to generate AI outputs creates no dataset sent back to model providers.

This is not a privacy feature. It is the architectural foundation of digital independence.

Research Foundation

This server implements patterns validated by peer-reviewed research on offline-first AI for bandwidth-constrained environments:

arXiv:2603.03339 (2026) — Offline-First LLM Architecture for Adaptive Learning in Low-Connectivity Environments — confirms that meaningful AI support is achievable with hardware-aware model selection when designed for infrastructure-limited deployment. Key finding: offline-first is a complementary paradigm, not a compromise.

Design principles applied:

  • Local-first: all core operations execute without internet connectivity
  • Graceful degradation: reduced functionality beats no functionality
  • JSONL queue: events accumulated offline sync when connectivity returns
  • Hardware-aware: tool selection adapts to available compute

East Africa context: Kenya, Tanzania, Uganda — rural areas with intermittent connectivity represent the primary deployment target. This server is not designed for ideal conditions. It is designed for real ones.

Install

pip install offline-mcp

Tools (6)

ToolDescription
check_ollama_statusCheck if Ollama is running locally and list available models
run_local_inferenceRun a prompt through a local Ollama model
list_recommended_modelsBest open-weight models for East Africa use cases
degraded_mode_guide4-level degraded mode architecture for offline operation
open_weights_directoryDirectory of open-weight models with Africa language support
local_deployment_guideDeployment guide for laptop, server, Raspberry Pi, Android

Context

Runs on a 50W solar panel + Raspberry Pi 4. Viable for rural Kenya clinics, schools, and community offices.

→ The Nairobi Stack

License

MIT © Gabriel Mahia | [email protected]

Part of the East Africa Coordination Stack

This MCP server is part of the Kenya coordination infrastructure. Connect it to africa-coord-bus — the coordination event bus that routes signals between domains automatically.

pip install africa-coord-bus

All servers: pypi.org/user/gmahia Live demo: coord-cascade-demo

IP & Collaboration

MIT licensed. Feedback via GitHub Issues only — pull requests are not accepted. Demo data is labeled DEMO and is not suitable for operational decisions. Full policy: docs/architecture/IP_POLICY.md. Security reports: see SECURITY.md.

Part of the East Africa coordination stack

Model-agnostic by design: closed APIs, open-weight models, and small distilled models are all first-class citizens.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.