shared-context-server 1.1.6

Shared Context MCP Server for multi-agent collaboration

Shared Context Server

Content Navigation

Symbol	Meaning	Time Investment
🚀	Quick start	2-5 minutes
⚙️	Configuration	10-15 minutes
🔧	Deep dive	30+ minutes
💡	Why this works	Context only
⚠️	Important note	Read carefully

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🎯 Quick Understanding (30 seconds)

A shared workspace for AI agents to collaborate on complex tasks.

The Problem: AI agents work independently, duplicate research, and can't build on each other's discoveries.

The Solution: Shared sessions where agents see previous findings and build incrementally instead of starting over.

# Agent 1: Security analysis
session.add_message("security_agent", "Found SQL injection in user login")

# Agent 2: Performance review (sees security findings)
session.add_message("perf_agent", "Optimized query while fixing SQL injection")

# Agent 3: Documentation (has full context)
session.add_message("docs_agent", "Documented secure, optimized login implementation")

Each agent builds on previous work instead of starting over.

💡 Uses MCP Protocol: Model Context Protocol - the standard for AI agent communication (works with Claude Code, Gemini, VS Code, Cursor, and frameworks like CrewAI).

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🚀 Try It Now (2 minutes)

Prerequisites Check (30 seconds)

Choose your path:

• ✅ Docker (recommended): docker --version works
• ✅ CLI (no Docker): python --version shows 3.10+ and uv --version works

Environment Configuration Templates

Choose your .env template (for local development):

# 🚀 Quick Start (recommended) - Essential variables only
cp .env.minimal .env

# 🔧 Full Development - All development features
cp .env.example .env

# 🐳 Docker Deployment - Container-optimized paths
cp .env.docker .env

💡 Most users want .env.minimal - it contains only the 12 essential variables you actually need.

Step 1: Start Server

Option A: Docker (recommended)

# Quick start with make command (uses GHCR image)
git clone https://github.com/leoric-crown/shared-context-server.git
cd shared-context-server
cp .env.minimal .env
# Edit .env with your secure keys (see Step 2 below)
make docker-prod

# OR manual Docker run:
API_KEY=$(openssl rand -base64 32)
echo "Your API key: $API_KEY"
docker run -d --name shared-context-server -p 23456:23456 \
  -e API_KEY="$API_KEY" \
  -e JWT_SECRET_KEY="$(openssl rand -base64 32)" \
  -e JWT_ENCRYPTION_KEY="$(python -c 'from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())')" \
  ghcr.io/leoric-crown/shared-context-server:latest

Option B: CLI (no Docker)

# Clone and setup
git clone https://github.com/leoric-crown/shared-context-server.git
cd shared-context-server
uv sync

# Generate and save your API key
API_KEY=$(openssl rand -base64 32)
JWT_SECRET_KEY=$(openssl rand -base64 32)
JWT_ENCRYPTION_KEY=$(python -c 'from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())')

# Start server
API_KEY="$API_KEY" JWT_SECRET_KEY="$JWT_SECRET_KEY" JWT_ENCRYPTION_KEY="$JWT_ENCRYPTION_KEY" \
  uv run python -m shared_context_server.scripts.cli
echo "Your API key: $API_KEY"

Step 2: Create .env File (Optional - for local development)

# Create .env file with your keys
cat > .env << EOF
API_KEY=$API_KEY
JWT_SECRET_KEY=$(openssl rand -base64 32)
JWT_ENCRYPTION_KEY=$(python -c 'from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())')
EOF

# Run with .env file
docker run -d --name shared-context-server -p 23456:23456 \
  --env-file .env ghcr.io/leoric-crown/shared-context-server:latest

Step 3: Connect Your MCP Client

Replace YOUR_API_KEY_HERE with the key from Step 1:

# Claude Code (simple HTTP transport)
claude mcp add --transport http scs http://localhost:23456/mcp/ \
  --header "X-API-Key: YOUR_API_KEY_HERE"

# Gemini CLI
gemini mcp add scs http://localhost:23456/mcp -t http -H "X-API-Key: YOUR_API_KEY_HERE"

# Test connection
claude mcp list  # Should show: ✓ Connected

VS Code Configuration

Add to your existing .vscode/mcp.json (create if it doesn't exist):

{
  "servers": {
    "shared-context-server": {
      "type": "http",
      "url": "http://localhost:23456/mcp",
      "headers": {"X-API-Key": "YOUR_API_KEY_HERE"}
    }
  }
}

Cursor Configuration

Add to your existing .cursor/mcp.json (create if it doesn't exist):

{
  "mcpServers": {
    "shared-context-server": {
      "command": "mcp-proxy",
      "args": ["--transport=http", "http://localhost:23456/mcp/", "--header", "X-API-Key: YOUR_API_KEY_HERE"]
    }
  }
}

Step 4: Verify & Monitor

# Test your setup (30 seconds)
curl -X POST http://localhost:23456/mcp/tool/create_session \
  -H "X-API-Key: $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"purpose": "test setup"}'

# Expected output: {"success": true, "session_id": "sess_..."}

# View the dashboard
open http://localhost:23456/ui/  # Real-time session monitoring

✅ Success indicators:

• curl command returns {"success": true, "session_id": "..."}
• Dashboard shows "1 active session"
• MCP client shows ✓ Connected status

📊 Web Dashboard (MVP)

Real-time monitoring interface for agent collaboration:

• Live session overview with active agent counts
• Real-time message streaming without page refreshes
• Session isolation visualization to track multi-agent workflows
• Performance monitoring for collaboration efficiency

💡 Perfect for: Monitoring agent handoffs, debugging collaboration flows, and demonstrating multi-agent coordination to stakeholders.

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🔧 Choose Your Path

Are you...

├── 👨‍💻 Building a side project?
│   → [Simple Integration](#-simple-integration) (5 minutes)
│
├── 🏢 Planning enterprise deployment?
│   → [Enterprise Setup](#-enterprise-considerations) (15+ minutes)
│
├── 🎓 Researching multi-agent systems?
│   → [Technical Deep Dive](#-technical-architecture) (30+ minutes)
│
└── 🤔 Just evaluating the concept?
    → [Framework Integration Examples](#-framework-examples) (5 minutes)

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🚀 Simple Integration

Works with existing tools you already use:

Direct MCP Integration (Tested)

# Via Claude Code or any MCP client
claude mcp add-json shared-context-server '{"command": "mcp-proxy", "args": ["--transport=streamablehttp", "http://localhost:23456/mcp/"]}'

# Direct API usage
import httpx
client = httpx.AsyncClient()
session = await client.post("http://localhost:23456/mcp/tool/create_session",
                           json={"purpose": "agent collaboration"})

⚠️ Framework Integration Status: Direct MCP protocol tested. CrewAI, AutoGen, and LangChain integrations are conceptual - we welcome community contributions to develop and test these patterns.

➡️ Next: MCP Integration Examples

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⚙️ Framework Examples

Code Review Pipeline

1. Security Agent finds vulnerabilities → shares findings
2. Performance Agent builds on security context → optimizes safely
3. Documentation Agent documents complete solution

💡 Why this works: Each agent builds on discoveries instead of duplicating work.

Research & Implementation

1. Research Agent gathers requirements → shares insights
2. Architecture Agent designs using research → documents decisions
3. Developer Agent implements with full context

More examples: Collaborative Workflows Guide

What works: ✅ MCP clients (Claude Code, Gemini, VS Code, Cursor) What's conceptual: 🔄 Framework patterns (CrewAI, AutoGen, LangChain) - community contributions welcome

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🔧 What This Is / What This Isn't

✅ What this MCP server provides

• Real-time collaboration substrate for multi-agent workflows
• Session isolation with clean boundaries between different tasks
• MCP protocol compliance that works with any MCP-compatible agent framework
• Infrastructure layer that enhances existing orchestration tools

💡 Why MCP protocol? Universal compatibility - works with Claude Code, CrewAI, AutoGen, LangChain, and custom frameworks without vendor lock-in.

❌ What this MCP server isn't

• Not a vector database - Use Pinecone, Milvus, or Chroma for long-term storage
• Not an orchestration platform - Use CrewAI, AutoGen, or LangChain for task management
• Not for permanent memory - Sessions are for active collaboration, not archival

💡 Why this approach? We enhance your existing tools rather than replacing them - no need to rewrite your agent workflows.

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🏢 Enterprise Considerations

⚙️ Production Setup & Scaling

Development → Production Path

Development (SQLite)

• ✅ Zero configuration
• ✅ Perfect for prototyping
• ❌ Limited to ~5 concurrent agents

Production (PostgreSQL)

• ✅ High concurrency (20+ agents)
• ✅ Enterprise backup/recovery
• ❌ Requires database management

Enterprise Features Roadmap

• SSO Integration: SAML/OIDC support planned
• Audit Logging: Enhanced compliance logging
• High Availability: Multi-node deployment
• Advanced RBAC: Attribute-based permissions

Migration: Start with SQLite, migrate when you hit concurrency limits.

🔧 Security & Compliance

Current Security Features

• JWT Authentication: Role-based access control
• Input Sanitization: XSS and injection prevention
• Secure Token Management: Prevents JWT exposure vulnerabilities
• Message Visibility: Public/private/agent-only filtering

Enterprise Security Roadmap

• SSO Integration: SAML, OIDC, Active Directory
• Audit Trails: SOX, HIPAA-compliant logging
• Data Governance: Retention policies, geographic residency
• Advanced Encryption: At-rest and in-transit encryption

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🔧 Technical Architecture

Core Design Principles

Session-Based Isolation

What: Each collaborative task gets its own workspace Why: Prevents cross-contamination while enabling rich collaboration within teams

Message Visibility Controls

What: Four-tier system (public/private/agent-only/admin-only) Why: Granular information sharing - agents can have private working memory and shared discoveries

MCP Protocol Integration

What: Model Context Protocol compliance for universal compatibility Why: Works with any MCP-compatible framework without custom integration code

Performance Characteristics

Designed for Real-Time Collaboration

• <30ms message operations for smooth agent handoffs
• 2-3ms fuzzy search across session history
• 20+ concurrent agents per session
• Session continuity during agent switches

💡 Why these targets? Sub-30ms ensures imperceptible delays during agent handoffs, maintaining workflow momentum.

Scalability Considerations

• SQLite: Development and small teams (<5 concurrent agents)
• PostgreSQL: Production deployments (20+ concurrent agents)
• Connection pooling: Built-in performance optimization
• Multi-level caching: >70% cache hit ratio for common operations

Database & Storage

Architecture Decision: Database Choice

SQLite for Development

• ✅ Zero configuration
• ✅ Perfect for prototyping
• ❌ Single writer limitation

PostgreSQL for Production

• ✅ Multi-writer concurrency
• ✅ Enterprise backup/recovery
• ✅ Advanced indexing and performance
• ❌ Requires database administration

Database Backend

• Unified: SQLAlchemy Core (supports SQLite, PostgreSQL, MySQL)
• Development: SQLite with aiosqlite driver (fastest, simplest)
• Production: PostgreSQL/MySQL with async drivers (scalable, robust)

Migration Path: SQLAlchemy backend provides smooth transition to PostgreSQL when scaling needs arise.

💡 Why this hybrid approach? Optimizes for developer experience during development while supporting enterprise scale in production.

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📖 Documentation & Next Steps

🟢 Getting Started Paths

• Integration Guide - CrewAI, AutoGen, LangChain examples
• Quick Reference - Commands and common tasks
• Development Setup - Local development environment

🟡 Production Deployment

• Docker Setup - Container deployment guide
• API Reference - All 15+ MCP tools with examples
• Troubleshooting - Common issues and solutions

🔴 Advanced Topics

• Custom Integration - Build your own MCP integration
• Production Deployment - Docker and scaling strategies

All documentation: Documentation Index

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🚀 Development Commands

make help        # Show all available commands
make dev         # Start development server with hot reload
make test        # Run tests with coverage
make quality     # Run all quality checks
make docker-prod # Production Docker (GHCR image)
make docker      # Development Docker (local build + hot reload)

⚙️ Direct commands without make

# Development
uv sync && uv run python -m shared_context_server.scripts.dev

# Testing
uv run pytest --cov=src

# Quality checks
uv run ruff check && uv run mypy src/

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License

MIT License - Open source software for the AI community.

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Built with modern Python tooling and MCP standards. Contributions welcome!