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Connect local LLMs to standard MCP tools in an air-gapped desktop cockpit.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "OrbitMCP" yet — see the docs or source repo.
In OrbitMCP, connect a locally running Llama 3.2 instance to available standard MCP tools and show the current list of dispatchable tools.
A status result showing the local model connection and the list of connected, dispatchable MCP tools.
Without relying on the public internet, use OrbitMCP to let a local model call a standard MCP tool and output the invocation flow and a summary of the result.
An execution record of an offline tool call, including dispatch steps and a result overview.
Use OrbitMCP as an edge-native MCP control plane and report the currently connected local model, tools, and the next available dispatch actions.
A local control-plane style view summarizing the model, tool connections, and executable actions.
Developers or researchers can use it when they need LLM-and-tool workflows inside isolated networks or offline desktops. It connects local Llama 3.2 models with standard MCP tools while keeping execution edge-native and security-conscious.
When a team wants to connect a local model to multiple standard MCP tools, it can serve as a desktop cockpit and MCP control plane for centralized connection visibility and tool dispatch. This reduces operational fragmentation.
If you need a local LLM to autonomously invoke tools, it can act as an autonomous tool dispatcher. Its description emphasizes deterministic hardware safety, making it suitable for controlled local automation workflows.
It is a lightweight desktop MCP tool that connects local LLMs, such as Llama 3.2, to standard MCP tools. It also acts as an edge-native MCP control plane and autonomous tool dispatcher.
Based on the description, it explicitly targets an air-gapped desktop setup, so it is designed for isolated or offline desktop scenarios. The provided materials do not specify whether any extra network dependencies are needed.
Known prerequisites include a local LLM setup, with Llama 3.2 explicitly mentioned, and standard MCP tools. The current materials do not provide exact installation steps, runtime requirements, or key requirements; see the source repository.
Build custom analysis MCP tools via JSON with built-in safety and quality controls.
Enable web search and extensible microservice tools for LLM agents.
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Orchestrate multi-model agents for plan-code-review-deliver workflows.
Install, run, restart, and monitor local MCP servers on Windows.