Provides tools for querying and updating an enterprise employee database stored in SQLite.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Enterprise Data MCP Server Show me user statistics"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Enterprise-grade Model Context Protocol server with secure tool execution, hybrid retrieval, and human-in-the-loop approvals. Includes a Next.js UI, Docker deployment, and GitHub Actions validation.
flowchart TD
subgraph Frontend [The Manager's Dashboard]
UI[Next.js UI / page.tsx]
API[Next.js API Routes]
UI <-->|HTTP/JSON| API
end
subgraph Backend [The Engine & Security Desk]
Server[mcp_project/server.py\nStarlette + MCP Server]
Auth[mcp_project/auth.py\nSecurity & JWT]
DB_Connector[mcp_project/db.py\nRelational Connector]
Hybrid_Connector[mcp_project/hybrid.py\nAI Retrieval Connectors]
Server --- Auth
Server --- DB_Connector
Server --- Hybrid_Connector
end
subgraph Storage [The Storage Room / Databases]
SQLite[(SQLite\nUsers, Logs, Approvals)]
Qdrant[(Qdrant\nVector Search / Docs)]
Neo4j[(Neo4j\nGraph / Relationships)]
end
subgraph Clients [AI Agents]
LangGraph[Orchestrator\nlanggraph_demo.py]
end
API <-->|REST / SSE| Server
LangGraph <-->|MCP Protocol| Server
DB_Connector <--> SQLite
Hybrid_Connector <--> Qdrant
Hybrid_Connector <--> Neo4jRelated MCP server: OCP AI Custom HR MCP Server
This chart shows how sensitive actions (like deactivating a user) are safely paused and routed to a human for approval:
sequenceDiagram
actor AI as AI Agent
participant Server as Python Server (server.py)
participant DB as SQLite DB (db.py)
participant API as Next.js API
actor Human as Admin Manager (UI)
AI->>Server: Call tool: `deactivate_user(id: 5)`
Server->>Server: Check tool sensitivity
Note over Server: Action is dangerous!<br/>Suspending execution...
Server->>DB: Create `approval_request` (Status: Pending)
Server-->>AI: Return "Action suspended. Waiting for approval."
Human->>API: Load Dashboard (localhost:3000)
API->>Server: GET /api/approvals
Server->>DB: Fetch all pending approvals
DB-->>Server: [Request #1: Deactivate User #5]
Server-->>API: Returns approval list
API-->>Human: Displays pending Action #1 on screen
Human->>API: Clicks "Approve"
API->>Server: POST /api/approvals/1/approve
Server->>DB: Update request status to "Approved"
Server->>DB: Execute ACTUAL `deactivate_user` query
Server->>DB: Write success to `audit_logs`
Server-->>API: 200 OK Status
API-->>Human: UI Updates (Action Completed)SOPs and system logs model real enterprise workflows and incident response.
Graph entities and edges support relationship-aware retrieval for dependency reasoning.
Audit logs and approval requests enable traceability and HITL governance.
Resources: SOP catalog, recent logs, graph entities, plus schema.
Tools: deterministic JSON schema per tool, including sensitive tool gating.
Prompts: dynamic ops assistant prompt adapting to role and incident level.
OAuth/JWT verification at the MCP layer via JWKS.
Scope-based authorization per tool.
Full audit logging for all tool calls.
HITL approval queue for sensitive actions.
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txtsqlite3 enterprise_data.db < schema.sqlThe server auto-initializes the database if the file is missing.
set MCP_AUTH_REQUIRED=false
python mcp_project/server.py --transport sse --port 8000MCP_AUTH_REQUIRED=true|false
MCP_JWKS_URL=
MCP_ISSUER=
MCP_AUDIENCE=
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=passwordSee .env.example and next-app/.env.example.
python scripts/seed_hybrid.pycd next-app
npm install
npm run devMCP_BASE_URL=http://localhost:8000
MCP_DEV_TOKEN=<optional-dev-token>
GOOGLE_CLIENT_ID=<your-google-client-id>
GOOGLE_CLIENT_SECRET=<your-google-client-secret>
NEXTAUTH_SECRET=<random-32-byte-base64>
NEXTAUTH_URL=http://localhost:3000The UI uses NextAuth with Google OAuth. If OAuth is not configured, provide MCP_DEV_TOKEN and set MCP_AUTH_REQUIRED=false on the server.
docker compose up --buildServices:
MCP server on 8000
Next.js UI on 3000
Neo4j on 7474/7687
Qdrant on 6333
set MCP_API_URL=http://localhost:8000/api
set MCP_TOKEN=<your-jwt>
python orchestrator/langgraph_demo.pySee mcp_project/server.py for full tool definitions. Resources are exposed for SOPs, logs, graph entities, and schema.
GitHub Actions builds Python and Next.js, validates the schema, and builds Docker images. See .github/workflows/ci.yml.
The project also includes a Streamlit-based chat interface that was the v1 prototype before the Next.js rewrite. It connects to the same database via db.py and uses Groq LLM for tool-calling.
streamlit run mcp_project/app.pyRequires GROQ_API_KEY in your .env file.
mcp_project/server.py MCP server + REST proxy
mcp_project/app.py Streamlit chat prototype
mcp_project/db.py SQLite access layer
mcp_project/auth.py OAuth/JWT verification
mcp_project/hybrid.py Qdrant + Neo4j adapters
schema.sql data model
next-app Next.js ops console
orchestrator LangGraph agent demo
docker-compose.yml local stack
This server cannot be deployed
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