Context Engineering

You gain a practical, end-to-end platform for managing, optimizing, and prompting AI models with Context Engineering MCP. It enables you to define reusable context templates, analyze and refine prompts, and connect your local MCP server to clients like Claude Desktop for seamless, high-quality AI interactions.

How to use

You connect a client to the MCP server, define sessions and context windows, and then create or analyze contexts to optimize AI responses. Start by configuring the MCP server in your client, then use templates and multi-modal capabilities to prepare the exact information the AI should receive. You will measure quality, adjust prompts, and iterate to improve speed, accuracy, and consistency.

Typical usage patterns include creating a session for a specific AI task (for example, customer support or code reviews), opening a context window with a maximum token limit, adding structured context elements, and then analyzing or optimizing the context. You can also render reusable templates across scenarios and use multi-modal inputs like text, images, and documents to enrich the AI’s understanding.

How to install

Prerequisites: Make sure you have Node.js 16+ and Python 3.10+ installed. You also need a Gemini API key to enable certain AI analysis and optimization features.

1. Clone the project repository and navigate into the MCP server folder.

# Clone the repository
git clone https://github.com/ShunsukeHayashi/context_-engineering_MCP.git
cd "context engineering_mcp_server"

2. Set up environment variables, including the Gemini API key.

cp .env.example .env
echo "GEMINI_API_KEY=your_key_here" >> .env

3. Install dependencies and start the Quick Start workflow or the individual components.

# Option A: Quick start script (Recommended)
./quickstart.sh

# Option B: Manual setup
# Terminal 1 - Context Engineering API
cd context_engineering
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
python context_api.py

# Terminal 2 - MCP Server (for Claude Desktop)
cd mcp-server
npm install
node context_mcp_server.js

Additional sections

Deployment, security considerations, and troubleshooting are covered below to help you run MCP in production or diagnose common issues.

Deployment notes

When running MCP in a production environment, ensure you have proper API key management, rate limiting, and persistent storage. Use PostgreSQL for production data, and set up monitoring with Prometheus and Grafana. Prepare CI/CD workflows to automate testing and deployments.

Security considerations

Protect API endpoints and MCP connections with authentication, restrict access to internal networks, and rotate keys regularly. Validate inputs and monitor for anomalous usage to prevent misuse of prompts or contexts.

Troubleshooting tips

If you cannot connect from Claude Desktop, verify that the MCP server process is running, confirm the node script path is correct, and check that the environment variable GEMINI_API_KEY is set. Review logs to identify any startup errors or missing dependencies.

Notes on tooling and extensions

The platform provides a set of MCP tools for session management, context construction, analysis, optimization, and template rendering. You can extend functionality by creating templates, using multi-modal inputs, and integrating retrieval-augmented workflows.

Available tools

create_context_session

Create a new context session for a specific AI task or agent.

create_context_window

Open a context window with a defined maximum token limit to hold context elements.

add_context_element

Add a structured element to a context window, including content type and priority.

analyze_context

Evaluate the quality and coherence of a given context.

optimize_context

Improve a context based on goals such as clarity, relevance, and brevity.

auto_optimize_context

Automatically decide and apply the best optimization strategy for a context.

get_context_stats

Retrieve statistics about a context window, including token usage and element counts.

create_prompt_template

Create a reusable prompt template for future use.

generate_prompt_template

AI-assisted creation of a new prompt template based on purpose.

list_prompt_templates

List available prompt templates by category or tags.

render_template

Render a template with specific variable values for deployment.

workflow

Define and execute automated context engineering workflows.

auto_optimize_context

AI-driven optimization of contexts with multiple possible improvements.