io.github.gabrielmahia/offline-mcp
MCP server enabling offline-first agent operation over intermittent connectivity
Open source Open in the app JSON README (API)
About
MCP server enabling offline-first agent operation over intermittent connectivity
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- gabrielmahia
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.1.4
- Last push
- 2026-08-06T14:34:30Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 03:02:48
- Updated
- 2026-08-29 03:02:48
- Origin id
io.github.gabrielmahia/offline-mcp
README
# offline-mcp <!-- mcp-name: io.github.gabrielmahia/offline-mcp --> [](https://glama.ai/mcp/servers/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. [](https://pypi.org/project/offline-mcp/) [](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 one of 32 tools in 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 32 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). <!-- interconnect:v1 --> ## Part of the East Africa coordination stack - **Install & run:** `pip install reli-cli && reli list` — 33 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. <!-- /interconnect:v1 -->