Safetyops Mcp Server

SafetyOps MCP Server

> AI-powered safety incident knowledge base — plug your industrial accident data into any AI assistant in hours, not months.

[](LICENSE) [](https://www.python.org/downloads/) [](https://modelcontextprotocol.io) [](https://azure.microsoft.com/en-us/products/ai-services/ai-search)

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What is this?

A production-ready MCP (Model Context Protocol) server that turns your workplace incident database into a RAG-powered AI assistant — compatible with Copilot Studio, Claude, ChatGPT, and any MCP-compatible client.

Ask your AI agent:

• "What corrective actions should we take after a welding fire?" → structured IMMEDIATE / SHORT TERM / LONG TERM action plan with source citations
• "How many incidents occurred in the chemical industry last year?" → statistics with AI-generated insights
• "What are the main causes of falls at height?" → RAG answer with document references

No custom development needed for each AI client — one server, every AI tool.

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Why this is different from Azure AI Search direct connector

| Capability | Azure Search connector | SafetyOps MCP | |---|---|---| | Basic Q&A | ✅ | ✅ | | Document search with filters | ❌ | ✅ kb_search | | Statistics & trends by category | ❌ | ✅ kb_analyze | | Prioritized corrective action plan | ❌ | ✅ kb_suggest | | Custom domain logic & prompts | ❌ | ✅ | | Works with Claude, ChatGPT, custom agents | ❌ | ✅ | | Deployable for any client's private data | ❌ | ✅ |

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4 MCP Tools

| Tool | Trigger | What it does | |---|---|---| | kb_search | "find incidents about...", "list accidents where..." | Hybrid search (BM25 + vector) with OData filters. Returns ranked documents with metadata. | | kb_converse | "why...", "how...", "what causes..." | RAG Q&A — retrieves relevant incidents and generates an answer with [Document X] citations. | | kb_analyze | "how many...", "statistics", "breakdown by..." | Faceted aggregations by severity / hazard type / industry. Returns counts, percentages, AI insights. | | kb_suggest | "what should we do?", "corrective actions", "after this incident..." | Finds similar past incidents and generates a prioritized action plan: 🚨 IMMEDIATE (24h) / ⚠️ SHORT TERM (1 week) / 📋 LONG TERM — with source citations. |

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Architecture

┌─────────────────────────────────────────────────────────┐
│               AI Client Layer                           │
│   Copilot Studio │ Claude Desktop │ ChatGPT │ Custom    │
└────────────────────────┬────────────────────────────────┘
                         │  JSON-RPC 2.0 (MCP protocol)
                         ▼
┌─────────────────────────────────────────────────────────┐
│            SafetyOps MCP Server (FastAPI)               │
│                                                         │
│  POST /mcp  ──►  ApiKeyMiddleware                       │
│                       │                                 │
│               ToolRegistry.call()                       │
│          ┌────────────┼────────────┐──────────┐         │
│       kb_search  kb_converse  kb_analyze  kb_suggest    │
└──────────────┬──────────────────────┬───────────────────┘
               │                      │
               ▼                      ▼
    Azure AI Search            Azure OpenAI
    (hybrid retrieval)        (gpt-4o-mini)
    279+ incidents             RAG generation

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Quick Start

1. Clone & install

git clone https://github.com//safetyops-mcp-server.git
cd safetyops-mcp-server
pip install -e ".[dev]"

2. Configure Azure credentials

cp .env.example .env

Edit .env:

# Azure AI Search
AZURE_SEARCH_ENDPOINT=https://.search.windows.net
AZURE_SEARCH_INDEX=safetyops-kb-v2
AZURE_SEARCH_API_KEY=

# Azure OpenAI
AZURE_OPENAI_ENDPOINT=https://.openai.azure.com/
AZURE_OPENAI_CHAT_DEPLOYMENT=gpt-4o-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-large
AZURE_OPENAI_API_KEY=

# MCP Server Auth
MCP_API_KEYS=your-secret-key-here

3. Start the server

python -m uvicorn safetyops_mcp.app.main:app --port 8001 --reload

4. Test it

# Health check
curl http://localhost:8001/health

# List available tools
curl -X POST http://localhost:8001/mcp \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-secret-key-here" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

# Ask for corrective actions
curl -X POST http://localhost:8001/mcp \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-secret-key-here" \
  -d '{
    "jsonrpc": "2.0", "id": 2, "method": "tools/call",
    "params": {
      "name": "kb_suggest",
      "arguments": {
        "incident_description": "A worker was burned during welding operations",
        "language": "en"
      }
    }
  }'

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Connect to Copilot Studio

> Connect your Microsoft 365 AI assistant to real incident data in 5 minutes.

1. Open your agent in Copilot Studio
2. Go to Actions / Extensions → Model Context Protocol (MCP)
3. Click Add existing MCP server
4. Set the server URL:

• Local (with Cloudflare Tunnel): https://.trycloudflare.com/mcp
• Production (Azure Web App): https://.azurewebsites.net/mcp

1. Authentication:

• Type: API key
• Header name: X-API-Key
• Value: your MCP_API_KEYS value

1. Save → the 4 tools appear automatically

See [docs/copilot-studio.md](docs/copilot-studio.md) for detailed setup.

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Connect to Claude Desktop

Add to ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "safetyops": {
      "url": "https://.azurewebsites.net/mcp",
      "headers": { "X-API-Key": "" }
    }
  }
}

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Deploy to Production (Azure Web App)

# Production server
gunicorn -c gunicorn.conf.py safetyops_mcp.app.main:app

See [docs/azure-deployment.md](docs/azure-deployment.md) for full Azure App Service setup.

> Never commit .env. Use Azure Key Vault in production.

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Fully Adaptable to Your Business Needs

The 4 built-in tools are a starting point, not a ceiling. Every layer is designed to be modified or extended without touching the MCP core.

Adapt the existing tools

Each tool has two levers you can tune for your specific context:

1. Output format — change how results are presented to match your organization's language and standards:

| Tool | Default output | Example adaptation | |---|---|---| | kb_suggest | IMMEDIATE / SHORT TERM / LONG TERM | → PDCA cycle (Plan / Do / Check / Act) for ISO 45001 clients | | kb_converse | Free-form answer with [Document X] citations | → Structured report: Context / Root Cause / Recommendation | | kb_analyze | Statistics + AI paragraph | → Executive summary table ready for board presentations | | kb_search | Ranked document list | → Filtered by user role (manager sees aggregates, operator sees full details) |

2. Content & domain logic — the system prompts encode your domain expertise:

> The HSE system prompt in kb_suggest was designed for industrial safety. > Swap it for a pharma, nuclear, or maritime prompt and the tool becomes a domain expert in that field — no code change required.

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Add custom tools for your business case

The tool registry accepts any new tool in minutes. Examples of tools built for specific clients:

kb_report     → Auto-generate a formatted incident report (Word/PDF) from a description
kb_compare    → Compare two incidents side by side and identify common root causes
kb_deadline   → Track corrective action deadlines and flag overdue items
kb_escalate   → Score incident severity and route to the right team automatically
kb_translate  → Deliver the action plan in the local language of the site

Adding a tool = one Python file + register it in registry.py. The AI client discovers it automatically via tools/list — no client-side changes needed.

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What this means for your deployment

When we deploy for your organization, the tools are configured for your workflows, your terminology, and your output formats — not a generic template. The architecture makes this fast: customization is configuration, not reengineering.

---

safetyops_mcp/
├── app/
│   ├── main.py              # FastAPI entry point + /health
│   ├── auth.py              # API key middleware
│   ├── mcp_router.py        # JSON-RPC 2.0 dispatch (/mcp)
│   └── settings.py          # Pydantic settings
└── mcp/
    ├── jsonrpc.py            # JSON-RPC 2.0 models
    ├── registry.py           # Tool registration + dispatch
    └── tools/
        ├── _openai_client.py # Shared Azure OpenAI factory
        ├── kb_search.py      # Hybrid search tool
        ├── kb_converse.py    # RAG Q&A tool
        ├── kb_analyze.py     # Statistical analysis tool
        └── kb_suggest.py     # Corrective action plan tool

data/
└── accidents.csv             # Source dataset (China industrial accidents)

scripts/
├── normalize_china_data.py   # Data normalization pipeline
└── pipeline_blob_to_search.py # Azure Search indexing pipeline

docs/
├── azure-setup.md
├── azure-deployment.md
├── copilot-studio.md
└── runbook.md

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Adapt to Your Own Data

This template is designed to be deployed for any incident knowledge base:

1. Replace data/accidents.csv with your incident dataset
2. Adjust the field mapping in scripts/normalize_china_data.py
3. Re-run the indexing pipeline
4. The 4 MCP tools work immediately with your data — no code changes

Supported data sources: CSV, JSON, SQL export, SharePoint lists.

See [docs/azure-setup.md](docs/azure-setup.md) for the indexing guide.

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Use Cases & Industries

| Industry | Data source | Key tool | |---|---|---| | Manufacturing / HSE | OSHA API, internal SIRH | kb_suggest — corrective action plans | | Nuclear | IRSN, NRC event reports | kb_converse — regulatory Q&A | | Maritime | EMSA, MAIB accident reports | kb_search — incident lookup | | Pharmaceutical | FDA Warning Letters | kb_analyze — trend analysis | | Mining | MSHA USA database | kb_suggest + kb_analyze |

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Tech Stack

| Layer | Technology | |---|---| | MCP Server | FastAPI + Uvicorn (Python 3.10+) | | Knowledge Base | Azure AI Search (hybrid BM25 + vector) | | LLM | Azure OpenAI gpt-4o-mini | | Embeddings | text-embedding-3-large | | AI Client | Copilot Studio, Claude Desktop, ChatGPT | | Public Exposure | Cloudflare Tunnel (dev) / Azure Web App (prod) | | Demo UI | Streamlit |

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License

MIT — see [LICENSE](LICENSE).

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Built with the MCP open standard — works with every AI client today and tomorrow.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ANASSIJASSI
• Source: ANASSIJASSI/safetyops-mcp-server
• License: MIT

Install and usage instructions live in the source repository linked above.