<div align="center">
<img src="https://kumo-ai.github.io/kumo-sdk/docs/_static/kumo-logo.svg" height="40"/>
<h1>KumoRFM MCP Server</h1>
</div>
<div align="center">
<p>
<a href="https://kumorfm.ai">KumoRFM</a> •
<a href="https://github.com/kumo-ai/kumo-rfm/">Notebooks</a> •
<a href="https://kumo.ai/company/news/kumorfm-mcp/">Blog</a> •
<a href="https://kumorfm.ai">Get an API key</a>
</p>
🔬 MCP server to query KumoRFM in your agentic flows
</div>📖 Introduction
KumoRFM is a pre-trained Relational Foundation Model (RFM) that generates training-free predictions on any relational multi-table data by interpreting the data as a (temporal) heterogeneous graph.
It can be queried via the Predictive Query Language (PQL).
This repository hosts a full-featured MCP (Model Context Protocol) server that empowers AI assistants with KumoRFM intelligence.
This server enables:
- 🕸️ Build, manage, and visualize graphs directly from CSV or Parquet files
- 💬 Convert natural language into PQL queries for seamless interaction
- 🤖 Query, analyze, and evaluate predictions from KumoRFM (missing value imputation, temporal forecasting, etc) all without any training required
🚀 Installation
🐍 Traditional MCP Server
The KumoRFM MCP server is available for Python 3.10 and above. To install, simply run:
pip install kumo-rfm-mcpAdd to your MCP configuration file (e.g., Claude Desktop's mcp_config.json):
{
"mcpServers": {
"kumo-rfm": {
"command": "python",
"args": ["-m", "kumo_rfm_mcp.server"],
"env": {
"KUMO_API_KEY": "your_api_key_here"
}
}
}
}⚡ MCP Bundle
We provide a single-click installation via our MCP Bundle (MCPB) (e.g., for integration into Claude Desktop):
- Download the
dxtfile from here - Double click to install
The MCP Bundle supports Linux, macOS and Windows, but requires a Python executable to be found in order to create a separate new virtual environment.
Claude code
To include the server in claude code use:
claude mcp add --transport stdio kumo-rfm-mcp --env KUMO_API_KEY=<YOUR-API-KEY> -- python -m kumo_rfm_mcp.server --port 8000🎬 Claude Desktop Demo
See here for the transcript.
https://github.com/user-attachments/assets/56192b0b-d9df-425f-9c10-8517c754420f
🔬 Agentic Workflows
You can use the KumoRFM MCP directly in your agentic workflows:
<table> <tr> <th align="center"> <a href="https://docs.crewai.com/en/mcp/overview"> <img src="https://cdn.prod.website-files.com/66cf2bfc3ed15b02da0ca770/66d07240057721394308addd_Logo%20(1).svg" width="150" /> </a> <br/> [<a href="https://github.com/kumo-ai/kumo-rfm/blob/master/notebooks/ecom_agent.ipynb">Example</a>] </th> <td valign="top"><pre lang="python"><code> from crewai import Agent from crewai_tools import MCPServerAdapter from mcp import StdioServerParameters <br/> params = StdioServerParameters( command='python', args=['-m', 'kumo_rfm_mcp.server'], env={'KUMO_API_KEY': ...}, ) <br/> with MCPServerAdapter(params) as mcp_tools: agent = Agent( role=..., goal=..., backstory=..., tools=mcp_tools, ) </code></pre></td> </tr> <tr> <th align="center"> <a href="https://langchain-ai.github.io/langgraph/agents/mcp/"> <picture class="github-only"> <source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg"> <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg"> <img src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="250"> </picture> </a> <br/> [<a href="https://github.com/kumo-ai/kumo-rfm/blob/master/notebooks/insurance_agent.ipynb">Example</a>] </th> <td valign="top"><pre lang="python"><code> from langchain_mcp_adapter.client MultiServerMCPClient from langgraph.prebuilt import create_react_agent <br/> client = MultiServerMCPClient({ 'kumo-rfm': { 'command': 'python', 'args': ['-m', 'kumo_rfm_mcp.server'], 'env': {'KUMO_API_KEY': ...}, } }) <br/> agent = create_react_agent( llm=..., tools=await client.get_tools(), ) </code></pre></td> </tr> <tr> <th align="center"> <a href="https://openai.github.io/openai-agents-python/mcp/"> <picture class="github-only"> <source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/a28d3311-d676-4b2f-923e-49d59fa00dfa"> <source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/27bde36e-e0cc-4944-93f6-66e432df2180"> <img src="https://github.com/user-attachments/assets/a28d3311-d676-4b2f-923e-49d59fa00dfa" width="180" /> </picture> </a> <br/> [<a href="https://github.com/kumo-ai/kumo-rfm/blob/master/notebooks/simple_sales_agent.ipynb">Example</a>] </th> <td valign="top"><pre lang="python"><code> from agents import Agent from agents.mcp import MCPServerStdio <br/> async with MCPServerStdio(params={ 'command': 'python', 'args': ['-m', 'kumo_rfm_mcp.server'], 'env': {'KUMO_API_KEY': ...}, }) as server: agent = Agent( name=..., instructions=..., mcp_servers=[server], ) </code></pre></td> </tr> <tr> <th align="center"> <a href="https://docs.anthropic.com/en/docs/claude-code/sdk/sdk-python/"> <picture class="github-only"> <source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/b4f8fc8a-6d3f-44ba-9623-3dedb29c6a95"> <source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/4408e2ca-7e4b-4a4c-8bb6-eb00dd486315"> <img src="https://github.com/user-attachments/assets/b4f8fc8a-6d3f-44ba-9623-3dedb29c6a95" width="180" /> </picture> </a> </th> <td valign="top"><pre lang="python"><code> from claude_code_sdk import query, ClaudeCodeOptions <br/> mcp_servers = { 'kumo-rfm': { 'command': 'python', 'args': ['-m', 'kumo_rfm_mcp.server'], 'env': {'KUMO_API_KEY': ...}, } } <br/> async for message in query( prompt=..., options=ClaudeCodeOptions( system_prompt=..., mcp_servers=mcp_servers, permission_mode='default', ), ): ... </code></pre></td> </tr> </table>Browse our examples to get started with agentic workflows powered by KumoRFM.
📚 Available Tools
I/O Operations
- 🔍
find_table_files- Searching for tabular files: Find all table-like files (e.g., CSV, Parquet) in a directory. - 🧐
inspect_table_files- Analyzing table structure: Inspect the first rows of table-like files.
Graph Management
- 🗂️
inspect_graph_metadata- Reviewing graph schema: Inspect the current graph metadata. - 🔄
update_graph_metadata- Updating graph schema: Partially update the current graph metadata. - 🖼️
get_mermaid- Creating graph diagram: Return the graph as a Mermaid entity relationship diagram. - 🕸️
materialize_graph- Assembling graph: Materialize the graph based on the current state of the graph metadata to make it available for inference operations. - 📂
lookup_table_rows- Retrieving table entries: Lookup rows in the raw data frame of a table for a list of primary keys.
Model Execution
- 🤖
predict- Running predictive query: Execute a predictive query and return model predictions. - 📊
evaluate- Evaluating predictive query: Evaluate a predictive query and return performance metrics which compares predictions against known ground-truth labels from historical examples. - 🧠
explain- Explaining prediction: Execute a predictive query and explain the model prediction.
🔧 Configuration
Environment Variables
KUMO_API_KEY: Authentication is needed once before predicting or evaluating with the
KumoRFM model.
You can generate your KumoRFM API key for free here.
If not set, you can also authenticate on-the-fly in individual session via an OAuth2 flow.
We love your feedback! :heart:
As you work with KumoRFM, if you encounter any problems or things that are confusing or don't work quite right, please open a new :octocat:issue.
You can also submit general feedback and suggestions here.
Join our Slack!
