🧠 PiMCP — Distributed Model Context Protocol System

PiMCP is a distributed implementation of the Model Context Protocol (MCP) designed to connect AI models (LLMs or agents) with distributed tools, data, and environments — across both cloud and edge devices such as Raspberry Pi clusters.

It provides a unified, scalable, and fault-tolerant protocol layer that enables multiple nodes to cooperate, share context, and execute external tool calls on behalf of models.

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🚀 Overview

PiMCP (Distributed Model Context Protocol System) enables LLMs and agents to access and coordinate distributed resources — such as tools, APIs, and context databases — across both edge devices and cloud nodes

It acts as a middleware layer between AI models and the physical or virtual world, orchestrating communication, context synchronization, and task execution through the MCP (Model Context Protocol).

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System Architecture

                     ┌────────────────────────────┐
                     │          Clients           │
                     │ (OpenAI, Claude, LocalLLM) │
                     └─────────────┬──────────────┘
                                   │
                                   ▼
                 ┌──────────────────────────────────┐
                 │        PiMCP Gateway Layer        │
                 │ (FastAPI + Redis Pub/Sub + JWT)   │
                 └────────────────┬──────────────────┘
                                  │
     ┌────────────────────────────┴────────────────────────────┐
     │                                                         │
┌───────────────┐                                     ┌───────────────┐
│  MCP Node A   │                                     │  MCP Node B   │
│  (Tokyo Edge) │                                     │  (SF Cloud)   │
│ - local tools │                                     │ - GCP tools   │
│ - log agent   │                                     │ - DB adapter  │
│ - vector db   │◀───── Redis / etcd Context Sync ───▶│ - shell exec  │
└───────────────┘                                     └───────────────┘
     │                                                         │
     ▼                                                         ▼
 Local Tools: Shell, FS, AI Inference              Cloud Tools: SQL, API, Storage

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🧠 Core Capabilities

• 🔄 Context Synchronization: Maintain distributed shared memory between nodes for context continuity.

• ⚙️ Tool Invocation Routing: Automatically find and execute the best tool handler across multiple nodes.

• 📡 Distributed Execution: Offload heavy computations to cloud nodes, while low-latency operations stay on edge.

• 🧩 Modular Plug-in Architecture: Each node exposes capabilities (file.search, shell.run, db.query, etc.) that can be dynamically registered or disabled.

-🔐 Secure Communication: All MCP traffic is authenticated via JWT and encrypted via TLS/mTLS.