ModuleContextStreaming (MCS) 🚀

A high-performance gRPC service designed to provide real-time, streamable context to Large Language Models (LLMs). This project serves as a robust backend for AI applications that require secure, low-latency communication with server-side tools and resources.

It features a secure, authenticated API and a powerful adapter for integrating with Model Context Protocol (MCP) servers, allowing you to expose tools from any MCP-compatible backend through a single, unified gRPC interface.

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Why Use MCS?

• Solve LLM Latency: Traditional REST APIs can be slow for conversational AI. MCS uses gRPC streaming to send data back to the LLM as it's generated, creating a more responsive user experience.
• Unify Your Tools: Stop building one-off backends. MCS can act as a secure gateway to all your tools, whether they are native Python functions or external Model Context Protocol (MCP) servers.
• Secure by Default: Don't worry about boilerplate security code. MCS provides out-of-the-box JWT authentication via Keycloak and enforces TLS encryption.
• Type-Safe & Reliable: By using a Protobuf schema, you eliminate entire classes of bugs. The API contract is clear, versionable, and reliable.

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High-Level Architecture

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Features

• High-Performance Streaming: Built on gRPC and HTTP/2 for efficient, multiplexed, and low-latency data streaming.
• Strict API Contract: Uses Protocol Buffers (.proto) as the single source of truth for the API, ensuring type-safe communication.
• Secure by Default:
  • JWT Authentication: Integrates with Keycloak for robust, token-based authentication using a gRPC interceptor.
  • TLS Encryption: Supports secure gRPC channels out-of-the-box for encrypted client-server communication.
• MCP Backend Integration: Seamlessly connect to and expose tools from MCP servers running over stdio.
• Intelligent Client: Includes an MCP-aware parser that can automatically distinguish between text, images, structured JSON, resources, and errors from tool responses.
• Configurable & Extensible: The server and client are configured via environment variables (.env file) and the server's Tool Registry makes it simple to add new native Python tools.

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Project Structure

ModuleContextStreaming/         # The project root directory
├── ModuleContextStreaming/     # The main, installable Python package
│   ├── init.py
│   ├── server.py             # Reusable gRPC Server class with MCP adapter
│   ├── client.py             # Reusable gRPC Client class with MCP parser
│   ├── auth.py               # Keycloak JWT authentication interceptor
│   ├── exceptions.py         # Custom exception types
│   ├── mcs_pb2.py            # Generated Protobuf messages
│   └── mcs_pb2_grpc.py       # Generated gRPC client/server stubs
├── protos/
│   └── mcs.proto             # The API contract source of truth
├── examples/
│   ├── simple_server.py      # Example runnable server with native tools
│   └── simple_client.py      # Example runnable client
├── certs/
│   ├── private.key           # Placeholder for your TLS private key
│   └── certificate.pem       # Placeholder for your TLS certificate
├── build_scripts.py          # Script to generate and patch gRPC code
├── DESIGN.md                 # Project architecture and design decisions
├── pyproject.toml            # Project metadata and dependencies
└── README.md

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Setup and Installation

Prerequisites

• Python 3.10+
• Git
• (Optional) OpenSSL - for self-signed certificates

Installation Steps

1. Clone the repository:

   git clone [https://github.com/armstrongsam25/ModuleContextStreaming.git](https://github.com/armstrongsam25/ModuleContextStreaming.git)
   cd ModuleContextStreaming

2. Create and activate a virtual environment:

   python -m venv .venv
   
   # On Windows: 
   .\.venv\Scripts\activate
   
   # On macOS/Linux: 
   source .venv/bin/activate

3. Install dependencies: This installs the project in editable mode (-e) along with all development dependencies.

   pip install -e .[dev]

4. Generate gRPC Code: Run these commands to compile the .proto file and apply the necessary import patch. You only need to re-run this when protos/mcs.proto changes.

   # 1. Generate gRPC code from the .proto file
   python -m grpc_tools.protoc -I ./protos --python_out=./ModuleContextStreaming --grpc_python_out=./ModuleContextStreaming mcs.proto
   
   # 2. Patch the generated code for correct relative imports
   python build_scripts/build.py

5. Generate Self-Signed Certificates (for local testing):

   openssl req -x509 -newkey rsa:4096 -keyout certs/private.key -out certs/certificate.pem -sha256 -days 365 -nodes -subj "/CN=localhost"

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Usage

1. Configure your environment: Copy .env.example to .env and fill in your Keycloak details.
2. Start the Server: In your first terminal, run:

   python examples/simple_server.py

3. Run the Client: In a second terminal, run:

   python examples/simple_client.py

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Contributing

Contributions are welcome! Please feel free to open an issue to report a bug or request a feature, or submit a pull request.

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Roadmap

• Advanced Authorization: Implement role-based access control (RBAC) based on JWT claims.
• TCP-based MCP Backends: Extend the MCPToolAdapter to support TCP sockets.
• Enhanced Observability: Integrate structured logging and OpenTelemetry.
• Comprehensive Testing: Add a full suite of unit and integration tests.
• PyPI Packaging: Finalize packaging and publish to PyPI.

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License

Distributed under the MIT License.