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Welcome¶
The MCP Gateway Masterclass is a hands-on workshop for building enterprise-grade agentic AI. You’ll bring up an MCP Gateway, register tools, add guardrails, and finish with a CrewAI + Langflow capstone running end-to-end.
Format at a glance
2 days × (4h theory AM + 4h labs PM). All examples use port 4444 for the gateway to build muscle memory.
Quick Start¶
python3 -m venv .venv && source .venv/bin/activate
pip install -U mcp-contextforge-gateway
mcpgateway --host 0.0.0.0 --port 4444
curl -s http://localhost:4444/health | jq .
export MCPGATEWAY_BEARER_TOKEN=$(python3 -m mcpgateway.utils.create_jwt_token \
--username [email protected] --exp 10080 --secret my-test-key)
mcp --server http://localhost:4444 tools list
Workshop Map¶
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Syllabus
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Two days from quickstart to production patterns, with checklists for each block. Open »
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Part I — Foundations
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MCP, gateway architecture, serving patterns, security & observability. Open »
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Part II — Day-1 Labs (with solutions)
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Register servers, use clients, build wrappers, and enable rate-limits. Open »
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Part III — Capstone Theory
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Design: Langflow ↔ Adapter ↔ Gateway ↔ CrewAI. Policies, RBAC, tracing. Open »
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Part IV — Capstone Build (with solutions)
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Implement the adapter, run the CrewAI agent through the gateway, add guardrails & RBAC, capture traces. Open »
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Appendices
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Docling + watsonx.ai RAG via the gateway, verified commands, API cheatsheets, troubleshooting, instructor RoS. Open »
Architecture at a Glance¶
flowchart LR
A[CrewAI Agent] --> B(MCP Gateway)
B --> C[Adapter/Server]
C --> D[Langflow API\n/run flow]
B -->|Guardrails: rate-limit, PII/secrets, RBAC| B
B --> E[(Logs & Traces)]