Context-Engineering-for-Multi-Agent-Systems

This MCP server implements the Model Context Protocol (MCP) to orchestrate a universal Context Engine for multi-agent systems. It enables you to define semantic blueprints, deploy specialized agents (for planning, execution, measurement, and moderation), and visualize reasoning through an observable trace dashboard. Use the built-in tooling to create domain-agnostic workflows where agents interact via clearly defined contexts, citations, and policies. The server exposes interfaces for configuring agent roles, token/cost analytics, and step-by-step reasoning traces, making governance, debugging, and auditing straightforward. You can run cross-domain scenarios (e.g., legal, marketing, or operations) without changing core code, simply by swapping the domain-specific blueprints or data sources in the MCP configuration. To interact with the system, start the MCP server, feed it high-level goals, and monitor the trace dashboard to understand agent decisions, inputs, and outputs in real time.

Prerequisites:

• Node.js installed (recommended for this MCP server example).
• Access to a shell/terminal.
• Git to clone the repository.

Installation steps:

1. Clone the repository: git clone https://github.com/Denis2054/Context-Engineering-for-Multi-Agent-Systems.git cd Context-Engineering-for-Multi-Agent-Systems

2. Install dependencies (if a package.json exists for the MCP server portion): npm install

3. Prepare configuration:

   • Create or edit the MCP configuration file at the path specified in MCP_CONFIG_PATH (default path shown in the mcp_config).json
   • Set up any required environment variables (MCP_LOG_LEVEL, MCP_ENABLE_TRACE, MCP_DATA_DIR, MCP_CONFIG_PATH) as needed for your environment.

4. Run the MCP server:

   • npm run start (or use the node command defined in mcp_config, e.g., node Chapter10/server.js)

5. Verify operation:

   • Check the console output for startup messages.
   • Open the trace dashboard URL (as exposed by the server) to observe agent reasoning and token analytics.

Notes:

• If the repository uses a Python or Docker-based setup in your environment, adapt the commands accordingly (e.g., python -m module or docker run ...).

Tips and considerations:

• Ensure observability: enable trace dashboards and ensure log levels are set to capture inputs, outputs, and reasoning steps for each agent.
• Use domain-agnostic blueprints to reuse MCP configurations across different domains; swap only the semantic context and data sources.
• Verify citations and moderation: maintain source-verifiable references for all agent outputs and apply moderation controls to align outputs with policy.
• If you plan to deploy in production, consider secure storage for tokens and credentials, and enable access controls for the trace dashboards.
• Common issues: mismatched schema between context definitions and agent capabilities; fix by aligning all agents to the MCP interface and updating the domain blueprints accordingly.