Skip to main content

MCP Server Deployment & Authentication

Superset includes a built-in Model Context Protocol (MCP) server that lets AI assistants -- Claude, ChatGPT, and other MCP-compatible clients -- interact with your Superset instance. Through MCP, clients can list dashboards, query datasets, execute SQL, create charts, and more.

This guide covers how to run, secure, and deploy the MCP server.

Looking for user docs?

See Using AI with Superset for a guide on what AI can do with Superset and how to connect your AI client.

Quick Start​

Get the MCP server running locally and connect an AI client in three steps.

1. Start the MCP server​

The MCP server runs as a separate process alongside Superset:

superset mcp run --host 127.0.0.1 --port 5008
FlagDefaultDescription
--host127.0.0.1Host to bind to
--port5008Port to bind to
--debugoffEnable debug logging

The endpoint is available at http://<host>:<port>/mcp.

2. Set a development user​

For local development, tell the MCP server which Superset user to impersonate (the user must already exist in your database):

# superset_config.py
MCP_DEV_USERNAME = "admin"

3. Connect an AI client​

Point your MCP client at the server. For Claude Desktop, edit the config file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
  • Linux: ~/.config/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "superset": {
      "url": "http://localhost:5008/mcp"
    }
  }
}

Restart Claude Desktop. The hammer icon in the chat bar confirms the connection.

See Connecting AI Clients for Claude Code, Claude Web, ChatGPT, and raw HTTP examples.

Prerequisites​

  • Apache Superset 5.0+ running and accessible
  • Python 3.10+
  • The fastmcp package (pip install fastmcp)

Authentication​

The MCP server supports multiple authentication methods depending on your deployment scenario.

Development Mode (No Auth)​

Disable authentication and use a fixed user:

# superset_config.py
MCP_AUTH_ENABLED = False
MCP_DEV_USERNAME = "admin"

All operations run as the configured user.

warning

Never use development mode in production. Always enable authentication for any internet-facing deployment.

JWT Authentication​

For production, enable JWT-based authentication. The MCP server validates a Bearer token on every request.

Option A: RS256 with JWKS endpoint​

The most common setup for OAuth 2.0 / OIDC providers that publish a JWKS (JSON Web Key Set) endpoint:

# superset_config.py
MCP_AUTH_ENABLED = True
MCP_JWT_ALGORITHM = "RS256"
MCP_JWKS_URI = "https://your-identity-provider.com/.well-known/jwks.json"
MCP_JWT_ISSUER = "https://your-identity-provider.com/"
MCP_JWT_AUDIENCE = "your-superset-instance"

Option B: RS256 with static public key​

Use this when you have a fixed RSA key pair (e.g., self-signed tokens):

# superset_config.py
MCP_AUTH_ENABLED = True
MCP_JWT_ALGORITHM = "RS256"
MCP_JWT_PUBLIC_KEY = """-----BEGIN PUBLIC KEY-----
MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA...
-----END PUBLIC KEY-----"""
MCP_JWT_ISSUER = "your-issuer"
MCP_JWT_AUDIENCE = "your-audience"

Option C: HS256 with shared secret​

Use this when both the token issuer and the MCP server share a symmetric secret:

# superset_config.py
MCP_AUTH_ENABLED = True
MCP_JWT_ALGORITHM = "HS256"
MCP_JWT_SECRET = "your-shared-secret-key"
MCP_JWT_ISSUER = "your-issuer"
MCP_JWT_AUDIENCE = "your-audience"
warning

Store MCP_JWT_SECRET securely. Never commit it to version control. Use environment variables:

import os
MCP_JWT_SECRET = os.environ.get("MCP_JWT_SECRET")

JWT claims​

The MCP server validates these standard claims:

ClaimConfig KeyDescription
exp--Expiration time (always validated)
issMCP_JWT_ISSUERToken issuer (optional but recommended)
audMCP_JWT_AUDIENCEToken audience (optional but recommended)
sub--Subject -- primary claim used to resolve the Superset user

User resolution​

After validating the token, the MCP server resolves a Superset username from the claims. It checks these in order, using the first non-empty value:

  1. subject -- the standard sub claim (via the access token object)
  2. client_id -- for machine-to-machine tokens
  3. payload["sub"] -- fallback to raw payload
  4. payload["email"] -- email-based lookup
  5. payload["username"] -- explicit username claim

The resolved value must match a username in the Superset ab_user table.

Scoped access​

Require specific scopes in the JWT to limit what MCP operations a token can perform:

# superset_config.py
MCP_REQUIRED_SCOPES = ["mcp:read", "mcp:write"]

Only tokens that include all required scopes are accepted.

Custom Auth Provider​

For advanced scenarios (e.g., a proprietary auth system), provide a factory function. This takes precedence over all built-in JWT configuration:

# superset_config.py
def my_custom_auth_factory(app):
    """Return a FastMCP auth provider instance."""
    from fastmcp.server.auth.providers.jwt import JWTVerifier
    return JWTVerifier(
        jwks_uri="https://my-auth.example.com/.well-known/jwks.json",
        issuer="https://my-auth.example.com/",
        audience="superset-mcp",
    )

MCP_AUTH_FACTORY = my_custom_auth_factory

Connecting AI Clients​

Claude Desktop​

Local development (no auth):

{
  "mcpServers": {
    "superset": {
      "url": "http://localhost:5008/mcp"
    }
  }
}

With JWT authentication:

{
  "mcpServers": {
    "superset": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote@latest",
        "http://your-superset-host:5008/mcp",
        "--header",
        "Authorization: Bearer YOUR_TOKEN"
      ]
    }
  }
}

Claude Code (CLI)​

Add to your project's .mcp.json:

{
  "mcpServers": {
    "superset": {
      "type": "url",
      "url": "http://localhost:5008/mcp"
    }
  }
}

With authentication:

{
  "mcpServers": {
    "superset": {
      "type": "url",
      "url": "http://localhost:5008/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_TOKEN"
      }
    }
  }
}

Claude Web (claude.ai)​

  1. Open claude.ai
  2. Click the + button (or your profile icon)
  3. Select Connectors
  4. Click Manage Connectors > Add custom connector
  5. Enter a name and your MCP URL (e.g., https://your-superset-host/mcp)
  6. Click Add
info

Custom connectors on Claude Web require a Pro, Max, Team, or Enterprise plan.

ChatGPT​

  1. Click your profile icon > Settings > Apps and Connectors
  2. Enable Developer Mode in Advanced Settings
  3. In the chat composer, press + > Add sources > App > Connect more > Create app
  4. Enter a name and your MCP server URL
  5. Click I understand and continue
info

ChatGPT MCP connectors require a Pro, Team, Enterprise, or Edu plan.

Direct HTTP requests​

Call the MCP server directly with any HTTP client:

curl -X POST http://localhost:5008/mcp \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer YOUR_JWT_TOKEN' \
  -d '{"jsonrpc": "2.0", "method": "tools/list", "id": 1}'

Deployment​

Single Process​

The simplest setup: run the MCP server alongside Superset on the same host.

superset_config.py:

MCP_SERVICE_HOST = "0.0.0.0"
MCP_SERVICE_PORT = 5008
MCP_DEV_USERNAME = "admin"  # or enable JWT auth

# If behind a reverse proxy, set the public-facing URL so
# MCP-generated links (chart previews, SQL Lab URLs) resolve correctly:
MCP_SERVICE_URL = "https://superset.example.com"

Start both processes:

# Terminal 1 -- Superset web server
superset run -h 0.0.0.0 -p 8088

# Terminal 2 -- MCP server
superset mcp run --host 0.0.0.0 --port 5008

Nginx reverse proxy with TLS:

server {
    listen 443 ssl;
    server_name superset.example.com;

    ssl_certificate     /path/to/cert.pem;
    ssl_certificate_key /path/to/key.pem;

    # Superset web UI
    location / {
        proxy_pass http://127.0.0.1:8088;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }

    # MCP endpoint
    location /mcp {
        proxy_pass http://127.0.0.1:5008/mcp;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header Authorization $http_authorization;
    }
}

Docker Compose​

Run Superset and the MCP server as separate containers sharing the same config:

# docker-compose.yml
services:
  superset:
    image: apache/superset:latest
    ports:
      - "8088:8088"
    volumes:
      - ./superset_config.py:/app/superset_config.py
    environment:
      - SUPERSET_CONFIG_PATH=/app/superset_config.py

  mcp:
    image: apache/superset:latest
    command: ["superset", "mcp", "run", "--host", "0.0.0.0", "--port", "5008"]
    ports:
      - "5008:5008"
    volumes:
      - ./superset_config.py:/app/superset_config.py
    environment:
      - SUPERSET_CONFIG_PATH=/app/superset_config.py
    depends_on:
      - superset

Both containers share the same superset_config.py, so authentication settings, database connections, and feature flags stay in sync.

Multi-Pod (Kubernetes)​

For high-availability deployments, configure Redis so that replicas share session state:

superset_config.py:

MCP_STORE_CONFIG = {
    "enabled": True,
    "CACHE_REDIS_URL": "redis://redis-host:6379/0",
    "event_store_max_events": 100,
    "event_store_ttl": 3600,
}

When CACHE_REDIS_URL is set, the MCP server uses a Redis-backed EventStore for session management, allowing replicas to share state. Without Redis, each pod manages its own in-memory sessions and stateful MCP interactions may fail when requests hit different replicas.

Configuration Reference​

All MCP settings go in superset_config.py. Defaults are defined in superset/mcp_service/mcp_config.py.

Core​

SettingDefaultDescription
MCP_SERVICE_HOST"localhost"Host the MCP server binds to
MCP_SERVICE_PORT5008Port the MCP server binds to
MCP_SERVICE_URLNonePublic base URL for MCP-generated links (set this when behind a reverse proxy)
MCP_DEBUGFalseEnable debug logging
MCP_DEV_USERNAME--Superset username for development mode (no auth)
MCP_RBAC_ENABLEDTrueEnforce Superset's role-based access control on MCP tool calls. When True, each tool checks that the authenticated user has the required FAB permission before executing. Disable only for testing or trusted-network deployments.

Authentication​

SettingDefaultDescription
MCP_AUTH_ENABLEDFalseEnable JWT authentication
MCP_JWT_ALGORITHM"RS256"JWT signing algorithm (RS256 or HS256)
MCP_JWKS_URINoneJWKS endpoint URL (RS256)
MCP_JWT_PUBLIC_KEYNoneStatic RSA public key string (RS256)
MCP_JWT_SECRETNoneShared secret string (HS256)
MCP_JWT_ISSUERNoneExpected iss claim
MCP_JWT_AUDIENCENoneExpected aud claim
MCP_REQUIRED_SCOPES[]Required JWT scopes
MCP_JWT_DEBUG_ERRORSFalseLog detailed JWT errors server-side (never exposed in HTTP responses per RFC 6750)
MCP_AUTH_FACTORYNoneCustom auth provider factory (flask_app) -> auth_provider. Takes precedence over built-in JWT
MCP_USER_RESOLVERNoneCustom function (app, access_token) -> username to extract a Superset username from a validated JWT token. When None, the default resolver checks preferred_username, username, email, and sub claims in that order.

Response Size Guard​

Limits response sizes to prevent exceeding LLM context windows:

MCP_RESPONSE_SIZE_CONFIG = {
    "enabled": True,
    "token_limit": 25000,
    "warn_threshold_pct": 80,
    "excluded_tools": [
        "health_check",
        "get_chart_preview",
        "generate_explore_link",
        "open_sql_lab_with_context",
    ],
}
KeyDefaultDescription
enabledTrueEnable response size checking
token_limit25000Maximum estimated token count per response
warn_threshold_pct80Warn when response exceeds this percentage of the limit
excluded_toolsSee aboveTools exempt from size checking (e.g., tools that return URLs, not data)

Caching​

Optional response caching for read-heavy workloads. Requires Redis when used with multiple replicas.

MCP_CACHE_CONFIG = {
    "enabled": False,
    "CACHE_KEY_PREFIX": None,
    "list_tools_ttl": 300,       # 5 min
    "list_resources_ttl": 300,
    "list_prompts_ttl": 300,
    "read_resource_ttl": 3600,   # 1 hour
    "get_prompt_ttl": 3600,
    "call_tool_ttl": 3600,
    "max_item_size": 1048576,    # 1 MB
    "excluded_tools": [
        "execute_sql",
        "generate_dashboard",
        "generate_chart",
        "update_chart",
    ],
}
KeyDefaultDescription
enabledFalseEnable response caching
CACHE_KEY_PREFIXNoneOptional prefix for cache keys (useful for shared Redis)
list_tools_ttl300Cache TTL in seconds for tools/list
list_resources_ttl300Cache TTL for resources/list
list_prompts_ttl300Cache TTL for prompts/list
read_resource_ttl3600Cache TTL for resources/read
get_prompt_ttl3600Cache TTL for prompts/get
call_tool_ttl3600Cache TTL for tools/call
max_item_size1048576Maximum cached item size in bytes (1 MB)
excluded_toolsSee aboveTools that are never cached (mutating or non-deterministic)

Redis Store (Multi-Pod)​

Enables Redis-backed session and event storage for multi-replica deployments:

MCP_STORE_CONFIG = {
    "enabled": False,
    "CACHE_REDIS_URL": None,
    "event_store_max_events": 100,
    "event_store_ttl": 3600,
}
KeyDefaultDescription
enabledFalseEnable Redis-backed store
CACHE_REDIS_URLNoneRedis connection URL (e.g., redis://redis-host:6379/0)
event_store_max_events100Maximum events retained per session
event_store_ttl3600Event TTL in seconds

By default the MCP server exposes a lightweight tool-search interface instead of advertising every tool at once. This reduces the initial context sent to the LLM by ~70%, which lowers cost and latency. The AI client discovers tools on demand by calling search_tools and then invokes them via call_tool.

MCP_TOOL_SEARCH_CONFIG = {
    "enabled": True,
    "strategy": "bm25",        # "bm25" (natural language) or "regex"
    "max_results": 5,
    "always_visible": [        # Tools always listed (pinned)
        "health_check",
        "get_instance_info",
    ],
    "search_tool_name": "search_tools",
    "call_tool_name": "call_tool",
    "include_schemas": False,  # False=summary mode (name + parameters_hint)
    "compact_schemas": True,   # Strip $defs (only applies when include_schemas=True)
    "max_description_length": 300,
}
KeyDefaultDescription
enabledTrueEnable tool search. When False, all tools are listed upfront
strategy"bm25"Search ranking algorithm. "bm25" supports natural language; "regex" supports pattern matching
max_results5Maximum tools returned per search query
always_visibleSee aboveTools that always appear in list_tools, regardless of search
include_schemasFalseWhen False (default, "summary mode"), search results omit inputSchema entirely and include a lightweight parameters_hint listing top-level parameter names. Set to True to include the full inputSchema in search results. Full schemas are always used when a tool is actually invoked via call_tool.
compact_schemasTrueStrip $defs / $ref and replace with {"type": "object"} in search results to reduce token cost. Only takes effect when include_schemas=True — ignored in summary mode.
max_description_length300Truncate tool descriptions in search results (0 = no truncation). Applies in both summary and full-schema modes.
tip

Set enabled: False to revert to the traditional "show all tools at once" behavior, which some clients or workflows may prefer.

Tool search reduces the initial token cost from ~15–20K tokens (full catalog) down to ~4–5K tokens (pinned tools + search interface) — roughly 85% savings at the start of each conversation.

Session & CSRF​

These values are flat-merged into the Flask app config used by the MCP server process:

MCP_SESSION_CONFIG = {
    "SESSION_COOKIE_HTTPONLY": True,
    "SESSION_COOKIE_SECURE": False,
    "SESSION_COOKIE_SAMESITE": "Lax",
    "SESSION_COOKIE_NAME": "superset_session",
    "PERMANENT_SESSION_LIFETIME": 86400,
}

MCP_CSRF_CONFIG = {
    "WTF_CSRF_ENABLED": True,
    "WTF_CSRF_TIME_LIMIT": None,
}

Access Control​

RBAC Enforcement​

The MCP server respects Superset's full role-based access control (RBAC). Every authenticated user can only access the data and operations their Superset roles permit — the same rules that apply in the Superset UI apply through MCP.

Each tool declares one or more required FAB permissions. The table below maps tool groups to their permission requirements:

Tool groupRequired FAB permission
list_charts, get_chart_info, get_chart_data, get_chart_preview, generate_chart, update_chartcan_read on Chart (read), can_write on Chart (mutate)
list_dashboards, get_dashboard_info, generate_dashboard, add_chart_to_existing_dashboardcan_read on Dashboard (read), can_write on Dashboard (mutate)
list_datasets, get_dataset_info, create_virtual_datasetcan_read on Dataset (read), can_write on Dataset (mutate)
list_databases, get_database_infocan_read on Database
execute_sqlcan_execute_sql_query on SQLLab
open_sql_lab_with_contextcan_read on SQLLab
save_sql_querycan_write on SavedQuery
health_checkNone (public)

To disable RBAC checking globally (for trusted-network deployments or testing), set:

# superset_config.py
MCP_RBAC_ENABLED = False
warning

Disabling RBAC removes all permission checks from MCP tool calls. Only do this on isolated, internal deployments where all MCP users are trusted admins.

Audit Log​

All MCP tool calls are recorded in Superset's action log. You can view them at Settings → Action Log (admin only). Each log entry records:

  • The tool name (e.g., mcp.generate_chart.db_write)
  • The authenticated user
  • A timestamp

This makes MCP activity fully auditable alongside regular Superset activity. The action log uses the same event logger as the rest of Superset, so existing log ingestion pipelines (e.g., sending logs to Elasticsearch or a SIEM) capture MCP events automatically.

Middleware Pipeline​

Every MCP request passes through a middleware stack before reaching the tool function. The default stack (assembled in build_middleware_list() in server.py) is:

MiddlewarePurposeDefault
StructuredContentStripperMiddlewareStrips structuredContent from responses for Claude.ai bridge compatibilityEnabled
LoggingMiddlewareLogs each tool call with user, parameters, and durationEnabled
GlobalErrorHandlerMiddlewareCatches unhandled exceptions and sanitizes sensitive data before it reaches the clientEnabled
ResponseSizeGuardMiddlewareEstimates token count, warns at 80% of limit, blocks at limitEnabled (configurable via MCP_RESPONSE_SIZE_CONFIG)
ResponseCachingMiddlewareCaches read-heavy tool responses (in-memory or Redis)Disabled (enable via MCP_CACHE_CONFIG)

Additional middleware classes (RateLimitMiddleware, FieldPermissionsMiddleware, PrivateToolMiddleware) are implemented in superset/mcp_service/middleware.py but are not added to the default pipeline. They are available for operators who want to layer them in via a custom startup path.

Error Sanitization​

The GlobalErrorHandlerMiddleware automatically redacts sensitive information from all error messages before they reach the LLM client. The following are replaced with generic messages:

  • Database connection strings — replaced with a generic connection error message
  • API keys and tokens — redacted from error traces
  • File system paths — stripped to prevent information disclosure
  • IP addresses — removed from error context

This ensures that a misconfigured database connection or an unexpected exception never leaks credentials or internal topology to the LLM or its users. All regex patterns used for redaction are bounded to prevent ReDoS attacks.

Performance​

Connection Pooling​

Each MCP server process maintains its own SQLAlchemy connection pool to the database. For multi-worker deployments, total open connections = workers × pool size.

# superset_config.py
SQLALCHEMY_POOL_SIZE = 5
SQLALCHEMY_MAX_OVERFLOW = 10
SQLALCHEMY_POOL_TIMEOUT = 30
SQLALCHEMY_POOL_RECYCLE = 3600  # Recycle connections after 1 hour

For a 3-pod Kubernetes deployment with the defaults above, expect up to 3 × (5 + 10) = 45 connections. Size your database's max_connections accordingly.

Response Caching​

Enable response caching for read-heavy workloads (dashboards/datasets that don't change frequently). With the in-memory backend (default when MCP_STORE_CONFIG is disabled), caching is per-process. Use Redis-backed caching for consistent cache hits across multiple pods:

MCP_CACHE_CONFIG = {"enabled": True, "call_tool_ttl": 3600}
MCP_STORE_CONFIG = {"enabled": True, "CACHE_REDIS_URL": "redis://redis:6379/0"}

Mutating tools (generate_chart, update_chart, execute_sql, generate_dashboard) are always excluded from caching regardless of this setting.

Troubleshooting​

Server won't start​

  • Verify fastmcp is installed: pip install fastmcp
  • Check that MCP_DEV_USERNAME is set if auth is disabled -- the server requires a user identity
  • Confirm the port is not already in use: lsof -i :5008

401 Unauthorized​

  • Verify your JWT token has not expired (exp claim)
  • Check that MCP_JWT_ISSUER and MCP_JWT_AUDIENCE match the token's iss and aud claims exactly
  • For RS256 with JWKS: confirm the JWKS URI is reachable from the MCP server
  • For RS256 with static key: confirm the public key string includes the BEGIN/END markers
  • For HS256: confirm the secret matches between the token issuer and MCP_JWT_SECRET
  • Enable MCP_JWT_DEBUG_ERRORS = True for detailed server-side logging (errors are never leaked to the client)

Tool not found​

  • Ensure the MCP server and Superset share the same superset_config.py
  • Check server logs at startup -- tool registration errors are logged with the tool name and reason

Client can't connect​

  • Verify the MCP server URL is reachable from the client machine
  • For Claude Desktop: fully quit the app (not just close the window) and restart after config changes
  • For remote access: ensure your firewall and reverse proxy allow traffic to the MCP port
  • Confirm the URL path ends with /mcp (e.g., http://localhost:5008/mcp)

Permission errors on tool calls​

  • The MCP server enforces Superset's RBAC permissions -- the authenticated user must have the required roles
  • In development mode, ensure MCP_DEV_USERNAME maps to a user with appropriate roles (e.g., Admin)
  • Check superset/security/manager.py for the specific permission tuples required by each tool domain (e.g., ("can_execute_sql_query", "SQLLab"))

Response too large​

  • If a tool call returns an error about exceeding token limits, the response size guard is blocking an oversized result
  • Reduce page_size or limit parameters, use select_columns to exclude large fields, or add filters to narrow results
  • To adjust the threshold, change token_limit in MCP_RESPONSE_SIZE_CONFIG
  • To disable the guard entirely, set MCP_RESPONSE_SIZE_CONFIG = {"enabled": False}

Audit Events​

All MCP tool calls are logged to Superset's event logger, the same system used by the web UI (viewable at Settings → Action Log). Each event captures:

  • Action: mcp.<tool_name>.<phase> (e.g., mcp.list_databases.query)
  • User: the resolved Superset username from the JWT or dev config
  • Timestamp: when the operation ran

This means MCP activity is auditable alongside normal user activity. No additional configuration is required — logging is on by default whenever the event logger is enabled in your Superset deployment.

Tool Pagination​

MCP list tools (list_datasets, list_charts, list_dashboards, list_databases) use offset pagination via page (1-based) and page_size parameters. Responses include page, page_size, total_count, total_pages, has_previous, and has_next. To iterate through all results:

# Example: fetch all charts across pages
all_charts = []
page = 1
while True:
    result = mcp.list_charts(page=page, page_size=50)
    all_charts.extend(result["charts"])
    if not result.get("has_next"):
        break
    page += 1

Security Best Practices​

  • Use TLS for all production MCP endpoints -- place the server behind a reverse proxy with HTTPS
  • Enable JWT authentication for any internet-facing deployment
  • RBAC enforcement -- The MCP server respects Superset's role-based access control. Users can only access data their roles permit
  • Secrets management -- Store MCP_JWT_SECRET, database credentials, and API keys in environment variables or a secrets manager, never in config files committed to version control
  • Scoped tokens -- Use MCP_REQUIRED_SCOPES to limit what operations a token can perform
  • Network isolation -- In Kubernetes, restrict MCP pod network policies to only allow traffic from your AI client endpoints
  • Review the Security documentation for additional extension security guidance

Next Steps​