Sigma MCP Server

🚀 mcp-server-sigma

Supercharge your AI Agents with native Sigma Computing superpowers! ⚡
An enterprise-grade Model Context Protocol (MCP) server with 155 tools covering connections, workbooks, data models, members, teams, deployments, webhooks, multi-tenant operations, and composite workflow recipes straight to your favorite AI assistant.

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⚠️ Disclaimers & Safety Warnings

[!IMPORTANT] Community Project Disclaimer
mcp-server-sigma is an independent open-source community project. It is not affiliated with, sponsored by, endorsed by, or supported by Sigma Computing, Inc. "Sigma Computing" is a trademark of Sigma Computing, Inc.

[!WARNING] Credentials & Safety Notice
This server uses API credentials scoped to your Sigma organization. Tools can mutate workbooks, users, teams, and data models.

• Read-Only Mode: To run safely without mutation risk, set SIGMA_MCP_READONLY=1 (grants 83 read-only tools).
• Destructive Safety Gates: All single-delete tools require explicit confirm=True. Bulk destructive operations (sigma_bulk_deactivate_members, sigma_bulk_remove_team_members) are disabled by default and require SIGMA_MCP_ALLOW_BULK_DESTRUCTIVE=1.
• Read SECURITY.md before deploying to production.

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💡 Why This Exists

Sigma Computing has a unique architectural asymmetry that shapes how you automate it:

1. Data Models are 100% Code-Representable: You can programmatically construct data models, define columns, joins, and SQL logic, update JSON specs, and swap warehouse sources via API.
2. Workbook Layouts are primarily UI-driven: While Sigma has introduced Beta endpoints for workbook specifications (/v2/workbooks/spec), programmatically constructing layout elements from scratch remains highly complex.

The canonical path to automated BI dashboards is:
Build the layout once in the Sigma UI, save it as a template, then instantiate and source-swap it programmatically forever after! 🎨 ➡️ 🤖

Our composite recipe tools (like sigma_deploy_template_to_folder and sigma_swap_workbook_sources) automate this exact pattern in a single MCP tool call (returning structured step progress or partial failure details if an intermediate step fails):

graph TD
    UI["Sigma UI"] -->|"1. Build Layout Once & Save"| TPL["Sigma Template"]
    Agent["AI Agent / LLM"] -->|"2. Call sigma_deploy_template_to_folder"| MCP["mcp-server-sigma"]
    MCP -->|"POST /v2/templates/{id}/instantiate"| API1["Instantiate Workbook"]
    MCP -->|"POST /v2/workbooks/{id}/swap_sources"| API2["Swap Warehouse Sources"]
    API2 -->|"Delivered"| Dest["Target Customer Folder"]

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📦 Quickstart & Installation

1. Install via pip or uv

pip install mcp-server-sigma
# or with uv
uv pip install mcp-server-sigma

Or run via Docker

docker run --rm -i --env-file .env \
  ghcr.io/christianclaudio/mcp-server-sigma:latest

2. Set Environment Variables

export SIGMA_CLIENT_ID="your-client-id"
export SIGMA_CLIENT_SECRET="your-client-secret"
export SIGMA_API_BASE_URL="https://api.us-a.aws.sigmacomputing.com"

[!TIP] Use the API base URL assigned to your organization's region.

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🔌 Integration Guides for AI Assistants & IDEs

mcp-server-sigma works seamlessly with all major AI assistants, IDEs, and CLI tools via standard stdio or streamable-http.

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "sigma": {
      "command": "sigma-mcp",
      "env": {
        "SIGMA_CLIENT_ID": "your-client-id",
        "SIGMA_CLIENT_SECRET": "your-client-secret",
        "SIGMA_API_BASE_URL": "https://api.us-a.aws.sigmacomputing.com"
      }
    }
  }
}

For Claude Code CLI:

claude mcp add sigma -- sigma-mcp

Add to your project's .agents/mcp_config.json (or global ~/.gemini/config/mcp_config.json):

{
  "mcpServers": {
    "sigma": {
      "command": "sigma-mcp",
      "args": [],
      "env": {
        "SIGMA_CLIENT_ID": "your-client-id",
        "SIGMA_CLIENT_SECRET": "your-client-secret",
        "SIGMA_API_BASE_URL": "https://api.us-a.aws.sigmacomputing.com"
      }
    }
  }
}

Run in network transport mode (Streamable HTTP) for local CLI & agent extensions:

# Source environment variables from a protected file or secret manager
source .env

# Launch server on HTTP localhost port 8000 for local clients
sigma-mcp --transport streamable-http --host 127.0.0.1 --port 8000

Point your local Codex / HTTP SSE client to http://127.0.0.1:8000/sse.

Note for hosted ChatGPT Actions or Custom GPTs: Hosted cloud services cannot reach localhost. Place an authenticating HTTPS proxy (e.g., ngrok, Cloudflare Tunnel, or Caddy with TLS and Auth) in front of the server before connecting cloud services.

Cline / Roo Code Settings (cline_mcp_settings.json):

{
  "mcpServers": {
    "sigma": {
      "command": "sigma-mcp",
      "env": {
        "SIGMA_CLIENT_ID": "your-client-id",
        "SIGMA_CLIENT_SECRET": "your-client-secret",
        "SIGMA_API_BASE_URL": "https://api.us-a.aws.sigmacomputing.com"
      }
    }
  }
}

Continue.dev Config (~/.continue/config.yaml):

mcpServers:
  - name: sigma
    command: sigma-mcp
    env:
      SIGMA_CLIENT_ID: "your-client-id"
      SIGMA_CLIENT_SECRET: "your-client-secret"
      SIGMA_API_BASE_URL: "https://api.us-a.aws.sigmacomputing.com"

Add .github/mcp.json to your repository:

{
  "mcpServers": {
    "sigma": {
      "type": "local",
      "command": "sigma-mcp",
      "env": {
        "SIGMA_CLIENT_ID": "${COPILOT_MCP_SIGMA_CLIENT_ID}",
        "SIGMA_CLIENT_SECRET": "${COPILOT_MCP_SIGMA_CLIENT_SECRET}",
        "SIGMA_API_BASE_URL": "https://api.us-a.aws.sigmacomputing.com",
        "SIGMA_MCP_READONLY": "1"
      },
      "tools": ["sigma_get_workbook", "sigma_list_workbooks", "sigma_get_data_model"]
    }
  }
}

Note for Copilot Cloud Agents: Cloud code-review integrations must be configured through Repository Settings > Copilot > MCP servers instead.

Add directly via the Cortex CLI:

cortex mcp add sigma-tools -- sigma-mcp

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🛡️ Safety & Security Controls

Configure behavior using environment variables:

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📊 Feature & Tool Summary

The server registers 155 tools by default across the following domain modules:

Note: Domain categories overlap slightly. The 2 bulk-destructive tools (sigma_bulk_deactivate_members, sigma_bulk_remove_team_members) are excluded by default and bring the total to 157 when enabled.

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🍳 Composite Workflow Recipes

These high-level tools bundle multi-step API sequences into a single atomic call:

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📐 MCP 2.0 Hints & Safety Annotations

Every tool includes structured MCP hints to assist AI clients with user permission prompts:

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🧮 Writing Sigma Formulas

AI models frequently hallucinate SQL or Excel functions when writing Sigma formulas (e.g. using ArrayAgg() instead of List()).
Before writing any Sigma formula, call the built-in reference tool:

# Model prompt helper
Use tool `sigma_formula_pitfalls` to check formula syntax rules.

See docs/formulas.md for full syntax details.

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👩‍💻 Local Development & Testing

# Install dev tools
pip install -e ".[dev]"

# Run full test suite with 100% statement line coverage enforcement
pytest --cov=src/sigma_mcp --cov-fail-under=100 --cov-report=term-missing

# Run OpenAPI drift check
python scripts/check_openapi_drift.py

# Run MCP tool contract validation
python scripts/check_tool_contract.py

# Code formatting & type checking
ruff check src/
ruff format --check .
mypy --strict src/

[!NOTE] Automated Drift Checks: This repository runs a weekly scheduled GitHub Action (sigma-drift-monitor.yml) that compares client methods against the live Sigma OpenAPI specification. If drift is detected, the workflow automatically opens an issue in the repository.

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🤝 Contributing & Community

Contributions are welcome! Please read CONTRIBUTING.md for development rules, SECURITY.md for security reporting, and CODE_OF_CONDUCT.md for community standards.

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📜 License

MIT License.
Copyright (c) 2026 Christian Claudio.

Disclaimer: Not affiliated with, sponsored by, or endorsed by Sigma Computing, Inc.