mcpField
M
Malloy MCP Server

Malloy MCP Server

Local
github.com/namabileData & Analytics2Updated 82w ago

About Malloy MCP Server

Malloy MCP Server lets data teams run Malloy queries and inspect Malloy project, package, and model metadata through Malloy Publisher.

Key features

  1. 01Run Malloy queries
  2. 02Read project, package, and model metadata
  3. 03Detailed query error context
  4. 04Type-safe implementation

Use cases

  1. 01Run a Malloy query from an MCP client
  2. 02Inspect Malloy model and package metadata

MCP support

It exposes a query execution tool and Malloy metadata resources to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Malloy MCP Server

An MCP server implementation for executing Malloy queries and managing Malloy resources.

Features

  • Execute Malloy queries via MCP
  • Access Malloy project, package, and model metadata
  • Robust error handling with detailed context
  • Comprehensive test coverage
  • Type-safe implementation

Installation

# Install using uv (recommended)
uv pip install malloy-mcp-server

# Or using pip
pip install malloy-mcp-server

Usage

Starting the Server
from malloy_mcp_server import mcp

# Run the server
if __name__ == "__main__":
    mcp.serve()
Configuration

The server can be configured using environment variables:

VariableDescriptionDefault
MALLOY_PUBLISHER_ROOT_URLURL of the Malloy Publisher APIhttp://localhost:4000

Example:

# Set the publisher URL
export MALLOY_PUBLISHER_ROOT_URL="http://malloy-publisher:4000"

# Run with custom configuration
python -m malloy_mcp_server
Executing Queries

The server provides an MCP tool for executing Malloy queries:

from malloy_mcp_server import ExecuteMalloyQueryTool

# Example query execution
result = await ExecuteMalloyQueryTool(
    query="select * from users",
    model_path="my_package/users"
)
Accessing Resources

The server provides the following resource endpoints:

  • malloy://project/home/metadata - Project metadata
  • malloy://project/home/package/{package_name} - Package metadata
  • malloy://project/home/model/{model_path} - Model metadata

Development

Setup
  1. Clone the repository:
git clone https://github.com/namabile/malloy-mcp-server.git
cd malloy-mcp-server
  1. Install dependencies:
uv pip install -e ".[dev]"
Running Tests
# Run all tests
pytest

# Run with coverage
pytest --cov=malloy_mcp_server
Code Quality

The project uses:

  • black for code formatting
  • mypy for type checking
  • ruff for linting

Run quality checks:

black .
mypy .
ruff check .

Error Handling

The server provides detailed error handling with context:

from malloy_mcp_server.errors import QueryExecutionError

try:
    result = await ExecuteMalloyQueryTool(...)
except QueryExecutionError as e:
    print(f"Error: {e.message}")
    print("Context:", e.context)

Architecture

The server is built on:

  • FastMCP for the MCP server implementation
  • Malloy Publisher Client for Malloy interactions
  • Pydantic for data validation

Key components:

  • server.py - Core server implementation
  • tools/query_executor.py - Query execution tool
  • errors.py - Error handling utilities

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests for new functionality
  5. Submit a pull request

License

MIT License - see LICENSE file for details

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.