offline-mcp

# offline-mcp
<!-- mcp-name: io.github.gabrielmahia/offline-mcp -->

[![offline-mcp Glama score](https://glama.ai/mcp/servers/gabrielmahia/offline-mcp/badges/score.svg)](https://glama.ai/mcp/servers/gabrielmahia/offline-mcp)
[![smithery badge](https://smithery.ai/badge/@gabrielmahia/offline-mcp)](https://smithery.ai/server/@gabrielmahia/offline-mcp)


---
**Compatible with `claude-sonnet-5`** (released 2026-06-30) — Anthropic's most agentic
Sonnet yet. Runs multi-step tool chains end-to-end without stopping short.
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](https://img.shields.io/badge/PyPI-v0.1.0-blue?logo=pypi)](https://pypi.org/project/offline-mcp/)
[![Thesis Layer](https://img.shields.io/badge/Thesis_Layer-L4_Offline_AI-red)](https://gabrielmahia.github.io/nairobi-stack)

**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
```bash
pip install offline-mcp
```

## Tools (6)
| Tool | Description |
|------|-------------|
| `check_ollama_status` | Check if Ollama is running locally and list available models |
| `run_local_inference` | Run a prompt through a local Ollama model |
| `list_recommended_models` | Best open-weight models for East Africa use cases |
| `degraded_mode_guide` | 4-level degraded mode architecture for offline operation |
| `open_weights_directory` | Directory of open-weight models with Africa language support |
| `local_deployment_guide` | Deployment 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](https://gabrielmahia.github.io/nairobi-stack)

## License
MIT © Gabriel Mahia | contact@aikungfu.dev

## Part of the East Africa Coordination Stack

This MCP server is part of the Kenya coordination infrastructure.
Connect it to [`africa-coord-bus`](https://github.com/gabrielmahia/africa-coord-bus) —
the coordination event bus that routes signals between domains automatically.

```bash
pip install africa-coord-bus
```

All servers: [pypi.org/user/gmahia](https://pypi.org/user/gmahia/)
Live demo: [coord-cascade-demo](https://github.com/gabrielmahia/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](docs/architecture/IP_POLICY.md). Security reports: see [SECURITY.md](SECURITY.md).

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## Part of the East Africa coordination stack

- **Install & run:** `pip install reli-cli && reli list` — the MCP servers on the [official MCP Registry](https://registry.modelcontextprotocol.io) under `io.github.gabrielmahia`
- **Evaluate any model on Swahili agent tasks:** [kipimo](https://github.com/gabrielmahia/kipimo) · [dataset](https://huggingface.co/datasets/gmahia/kipimo) · [leaderboard](https://huggingface.co/spaces/gmahia/kipimo-leaderboard)
- **Coordinate across servers:** [africa-coord-bus](https://pypi.org/project/africa-coord-bus/) — offline-first event bus with a built-in Kenya routing table
- **Datasets:** [huggingface.co/gmahia](https://huggingface.co/gmahia) · **Docs hub:** [nairobi-stack](https://github.com/gabrielmahia/nairobi-stack)

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