Best for
- Privacy-conscious users who want data to remain on-device
- Users needing local AI capabilities on mobile or Mac
- Users working with GGUF models or local-network model servers
O
Off Grid AI
Community
Not a standard server
An offline AI suite that runs on your device
This is not a typical connect-and-use MCP server; read the source repository before using it.
- Category
- Other #225 of 230
- Stars
- ★ 3.2k Very popular
- Runtime
- Node.js 20+ · Prebuilt binary
- Credentials
- No credential needed
- License
- MIT
- Last commit
25FMRS · D
The source clearly documents Off Grid AI as an offline, on-device AI application, but does not establish that the repository root contains an independently installable or callable MCP server. Tools, transports, client compatibility, and install configuration are therefore left unspecified.
Strongest · Maintenance 10/20 Weakest · Reliability 2/20
Why each score
- Reliability 2/20
- No server manifest or source files are provided, so MCP initialization, tool-list handshake, tool parameters, error handling, and an executable path cannot be verified. The README describes a mobile/desktop AI application and built-in tools, but does not establish that a runnable MCP server exists at the repository root; only a very low plausibility score is warranted.
- Security and permissions 7/20
- The README claims local processing, system-keychain storage, local-network servers, and user approval for actions involving calendar, email, and MCP services; these are positive signals. However, there is no reviewable server implementation showing permission boundaries, credential flow, network scope, or per-tool confirmation. The broad claim that no data leaves the device therefore cannot be validated for this MCP surface. No explicit red-line risk is shown in the supplied material.
- Maintenance 10/20
- The repository is not archived, uses the MIT license, and includes contribution guidance, CI badges, and a community channel, indicating some maintenance foundation. However, no commit history, release cadence, dependency-update policy, security-response process, or maintainer accountability is supplied. Badges alone do not verify sustained maintenance, so the score remains moderate-low.
- Documentation 4/20
- The README documents application capabilities, platforms, build prerequisites, test categories, and some operational limitations, including NPU compatibility and model-loading behavior. It provides no MCP manifest, server startup instructions, tool schemas, authentication configuration, client connection examples, error behavior, or MCP-specific troubleshooting. The core reviewable documentation is missing, so substantial points are deducted.
- Setup experience 2/20
- The supplied setup instructions build and install a React Native application, not an MCP server or client connection. There is no manifest, launch command, transport configuration, configuration example, or mainstream MCP-client integration guidance. The path from runtime preparation to a working MCP connection cannot be established, warranting a near-minimum score.
Static review · not runListed 2026-08-14
Read the FMRS scoring method →Fit and risk
What it can accessReads local filesUses the network
Not for
- Users seeking a server with explicitly documented MCP tools and transport configuration
- Users requiring cloud hosting, remote APIs, or verified MCP client compatibility
- Devices without sufficient hardware resources for the selected models
Required permissions
- Use device CPU, GPU, or NPU for local model inference
- Access the microphone for on-device Whisper transcription
- Access the camera for vision analysis
- Access local files or documents for attachments and knowledge-base processing
- Access the local network to connect to OpenAI-compatible remote LLM servers
Risks and side effects
- Model inference and image generation can consume substantial device resources and memory
- Hexagon NPU support is marked experimental; some quantizations or model architectures may fall back to CPU or produce garbled output
- When connected to a remote LLM server on the local network, data is sent to that service; the README does not specify its privacy policy
- Tool calling may perform searches or query device information and the local knowledge base; no separate MCP permission boundary is documented
Setup
Before you start
Runtime：Node.js 20+ · Prebuilt binary
The source provides no installation steps or client configuration for an independent MCP server. The README only describes installing the app from app stores, GitHub Releases, or building from the repository root. Building requires Node.js 20+, JDK 17 and Android SDK 36 for Android, and Xcode 15+ for iOS.
Check that it works
Launch the app and, in a conversation with a function-calling model, confirm the built-in tools (web search, calculator, date/time, search_knowledge_base) fire — asking for today's date and getting a correct answer proves the tool loop works.
Troubleshooting
- Confirm that the device meets the relevant Android, iOS, or macOS build requirements
- Check that the model quantization matches available GPU or NPU support
- If NPU output is incorrect, switch to CPU or a compatible GPU backend
- Check local-network connectivity and reachability of the OpenAI-compatible service
- Confirm that the app has access to the required microphone, camera, and documents
Things to try
Once connected, you can ask your AI assistant things like:
- Summarize this PDF I attached to the conversation
- Point the camera at this receipt and tell me the total
- Rewrite my one-line idea into a detailed Stable Diffusion prompt
- Search the web for today's news and give me clickable links
Use cases
Run local language models offline on a phone or Mac
Use on-device vision, speech transcription, and image generation
Use built-in web search, calculator, date/time, device information, and knowledge-base search tools
Connect to OpenAI-compatible LLM servers on a local network
Overview
Off Grid AI is a local AI application for Android, iOS, and macOS with GGUF text models, vision, Whisper speech-to-text, Stable Diffusion image generation, tool calling, document analysis, and local-network remote LLM support. The source does not provide an independent MCP server manifest, tool list, transport, or client configuration.
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Source revision 3118e75f5c47 Data synced 2026-10-11 Read the FMRS scoring method