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MCP server · AI

Metricflow

A Model Context Protocol (MCP) server that provides MetricFlow CLI tools

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Scored 6 months ago · breakdown

About Metricflow

Metricflow is an MCP server in the AI category: a Model Context Protocol (MCP) server that provides MetricFlow CLI tools.

Install

From source
git clone https://github.com/datnguye/mcp-metricflow

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Tools (7)

health_checksPerform system health checks

Parameters not published.

list_dimension_valuesList values for a dimension

Parameters not published.

list_dimensionsList available dimensions

Parameters not published.

list_entitiesList available entities

Parameters not published.

list_metricsList available metrics

Parameters not published.

queryExecute MetricFlow queries

Parameters not published.

validate_configsValidate model configurations

Parameters not published.

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Security checks

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README

mcp-metricflow

Python License Coverage

A Model Context Protocol (MCP) server that provides MetricFlow CLI tools through both SSE (with optional API key authentication) and STDIO interfaces.

[!WARNING] This repository is a learning project focused on MetricFlow integration with MCP. For production use cases, please refer to the official dbt-mcp implementation by dbt Labs.

Table of Contents

Overview

This project provides a Model Context Protocol (MCP) server that wraps MetricFlow CLI commands, making them accessible through both Server-Sent Events (SSE) and Standard Input/Output (STDIO) interfaces. It enables seamless integration with Claude Desktop and other MCP-compatible clients.

Setup

# Install uv at https://docs.astral.sh/uv/getting-started/installation/

# Copy environment template
cp .env.template .env
# ...and then jump to # Configuration section to fulfill it

Configuration

Edit the .env file with your specific configuration:

# Required: Path to your dbt project
DBT_PROJECT_DIR=/path/to/your/dbt/project e.g. /Users/dat/repos/il/jaffle-shop

# Optional: Other configurations
DBT_PROFILES_DIR=/path/to/.dbt
MF_PATH=mf
MF_TMP_DIR=/path/to/tmp

# SSE server configuration (optional)
MCP_HOST=localhost
MCP_PORT=8000

# API key authentication for SSE mode (optional)
MCP_API_KEY=your-secret-api-key
MCP_REQUIRE_AUTH=false

Running the MCP Server

STDIO Mode ✅

For integration with Claude Desktop (or any other MCP Client tool), use STDIO mode with the following uvx command:

uvx --env-file /path/to/.env --with "mcp-metricflow[snowflake]" mcp-metricflow

Sample .env file:

DBT_PROJECT_DIR=/Users/xxx/sources/jaffle-shop
DBT_PROFILES_DIR=/Users/xxx/.dbt
MF_TMP_DIR=/Users/xxx/.dbt/tmp

Add this configuration to the respective client's config file:

{
  "mcpServers": {
    "mcp-metricflow": {
      "command": "uvx",
      "args": [
        "--env-file", "<path-to-.env-file>",
        "--with", "mcp-metricflow[snowflake]",
        "mcp-metricflow"
      ]
    },
  }
}

NOTE: Currently only support Snowflake with this extra dependency specified: "--with", "mcp-metricflow[snowflake]"

NOTE: We might have to use absolute path for example: /Users/xxx/.local/bin/uvx instead of uvx alone. Use which uvx to get the full path

SSE Mode 🚧

For web-based integration or direct HTTP access:

# export DBT_PROFILES_DIR=~/.dbt
uv run python src/main_sse.py

The server will start on http://localhost:8000 (or the host/port specified in your environment variables).

API Key Authentication

The SSE server supports optional API key authentication. To enable authentication:

  1. Set the required environment variables:

    export MCP_API_KEY="your-secret-api-key"
    export MCP_REQUIRE_AUTH="true"
    
  2. Access authenticated endpoints by including the API key in the Authorization header:

    # Health check (no authentication required)
    curl http://localhost:8000/health
    
    # SSE endpoint (requires authentication when enabled)
    curl -H "Authorization: Bearer your-secret-api-key" http://localhost:8000/sse
    

Authentication Configuration:

  • MCP_API_KEY: The secret API key for authentication (required when MCP_REQUIRE_AUTH=true)
  • MCP_REQUIRE_AUTH: Enable/disable authentication (true, 1, yes, on to enable; default: false)

Security Notes:

  • The /health endpoint is always accessible without authentication for monitoring purposes
  • The /sse endpoint requires authentication when MCP_REQUIRE_AUTH=true
  • API keys are case-sensitive and support special characters
  • Store API keys securely and avoid committing them to version control

Available Tools

The MCP server exposes the following MetricFlow CLI tools:

Tool Description Required Parameters Optional Parameters
query Execute MetricFlow queries session_id, metrics group_by, start_time, end_time, where, order, limit, saved_query, explain, show_dataflow_plan, show_sql_descriptions
list_metrics List available metrics None search, show_all_dimensions
list_dimensions List available dimensions None metrics
list_entities List available entities None metrics
list_dimension_values List values for a dimension dimension, metrics start_time, end_time
validate_configs Validate model configurations None dw_timeout, skip_dw, show_all, verbose_issues, semantic_validation_workers
health_checks Perform system health checks None None

Each tool includes comprehensive documentation accessible through the MCP interface.

Project Structure

src/
├── config/
│   └── config.py              # Configuration management
├── server/
│   ├── auth.py                # API key authentication
│   ├── sse_server.py          # SSE server implementation
│   └── stdio_server.py        # STDIO server implementation
├── tools/
│   ├── prompts/mf_cli/        # Tool documentation (*.md files)
│   ├── metricflow/            # MetricFlow CLI wrappers
│   │   ├── base.py            # Shared command execution
│   │   ├── query.py           # Query functionality
│   │   ├── list_metrics.py    # List metrics
│   │   ├── list_dimensions.py # List dimensions
│   │   ├── list_entities.py   # List entities
│   │   ├── list_dimension_values.py # List dimension values
│   │   ├── validate_configs.py # Configuration validation
│   │   └── health_checks.py   # Health checks
│   └── cli_tools.py           # MCP tool registration
├── utils/
│   ├── logger.py              # Logging configuration
│   └── prompts.py             # Prompt loading utilities
├── main_sse.py                # SSE server entry point
└── main_stdio.py              # STDIO server entry point

Contributing ✨

If you've ever wanted to contribute to this tool, and a great cause, now is your chance!

See the contributing docs CONTRIBUTING for more information.

If you've found this tool to be very helpful, please consider giving the repository a star, sharing it on social media, or even writing a blog post about it 💌

mcp-metricflow stars buy me a coffee

Finally, super thanks to our Contributors:

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Metricflow

How do I install Metricflow?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Metricflow safe to use with an AI agent?

Its trust score is 34 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Metricflow still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.