Which integrations are available for this server?

Enables ChatGPT and OpenAI-based assistants to query and analyze workplace safety incidents using tools for hybrid search, RAG Q&A, statistical analysis, and corrective action suggestions.

How do I use SafetyOps MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@SafetyOps MCP Server suggest corrective actions for forklift accident" 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.

SafetyOps MCP Server

by ANASSIJASSI

Overview Schema Source Related Servers Discussions Admin

Python

Remote

SafetyOps MCP Server

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

License: MIT Python 3.10+ MCP Protocol Azure AI Search

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.

Related MCP server: Internal Documentation Search

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	❌	✅

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.

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

Quick Start

1. Clone & install

git clone https://github.com/<your-username>/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://<your-service>.search.windows.net
AZURE_SEARCH_INDEX=safetyops-kb-v2
AZURE_SEARCH_API_KEY=<your-search-admin-key>

# Azure OpenAI
AZURE_OPENAI_ENDPOINT=https://<your-resource>.openai.azure.com/
AZURE_OPENAI_CHAT_DEPLOYMENT=gpt-4o-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-large
AZURE_OPENAI_API_KEY=<your-openai-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"
      }
    }
  }'

Connect to Copilot Studio

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

Open your agent in Copilot Studio
Go to Actions / Extensions → Model Context Protocol (MCP)
Click Add existing MCP server
Set the server URL:
- Local (with Cloudflare Tunnel): https://<your-tunnel>.trycloudflare.com/mcp
- Production (Azure Web App): https://<your-app>.azurewebsites.net/mcp
Authentication:
- Type: API key
- Header name: X-API-Key
- Value: your MCP_API_KEYS value
Save → the 4 tools appear automatically

See docs/copilot-studio.md for detailed setup.

Connect to Claude Desktop

Add to ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "safetyops": {
      "url": "https://<your-app>.azurewebsites.net/mcp",
      "headers": { "X-API-Key": "<your-key>" }
    }
  }
}

Deploy to Production (Azure Web App)

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

See docs/azure-deployment.md for full Azure App Service setup.

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

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.

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.

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

Adapt to Your Own Data

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

Replace data/accidents.csv with your incident dataset
Adjust the field mapping in scripts/normalize_china_data.py
Re-run the indexing pipeline
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 for the indexing guide.

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`

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

License

MIT — see LICENSE.

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

This server cannot be deployed

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Maintenance

ActivityInactive

ResponsivenessNo issues

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SafetyOps MCP Server

What is this?

Why this is different from Azure AI Search direct connector

4 MCP Tools

Architecture

Quick Start

1. Clone & install

2. Configure Azure credentials

3. Start the server

4. Test it

Connect to Copilot Studio

Connect to Claude Desktop

Deploy to Production (Azure Web App)

Fully Adaptable to Your Business Needs

Adapt the existing tools

Add custom tools for your business case

What this means for your deployment

Adapt to Your Own Data

Use Cases & Industries

Tech Stack

License

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

AXYS MCP Lite

Internal Documentation Search

agentic-vision

local-docs-mcp