RCA-MCP

io.github.dave1362Published by GitHub account dave1362v4.1.20Updated Oct 24, 2026

Causal root-cause analysis MCP server -- 56 tools across graphs, RCA models, PyRCA.

VerifiedSTDIODesktop onlyData & Analytics AI & ML

Server details

Identifier: io.github.dave1362/rca-mcp-connector
Transport: STDIO
Runtime: Desktop only
Trust: Verified
Published: Sep 16, 2026

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Overview

AI-generated overview

A local MCP connector that gives an assistant 56 tools for causal root-cause analysis: building causal graphs, running RCA models and PyRCA algorithms, and gen

What you can do: Build and work with causal graphs for root-cause analysis. Run 10 RCA model families plus three PyRCA-based algorithms and compare results across models. Generate reports in PDF, HTML, Excel and Markdown formats.
When to use it: Useful when investigating why a system, service or metric failed and you want an assistant to build causal graphs, run root-cause models and produce a report, rather than just summarizing logs.
Requirements: Runs locally as a Python package (rca-mcp-connector) launched via uvx or pip. Needs an RCA-MCP API key in RCA_MCP_API_KEY and network access to the backend, whose base URL is set in RCA_MCP_API_URL. The README notes the default custom domain is not yet active, so the current backend URL must be configured.

Before you install

Requires an API key (RCA_MCP_API_KEY) that is sent to a third-party backend, so treat it as a secret. Analysis data is processed by that remote service. The README states the key is free and no credit card is required, but confirm the current terms before relying on it.

Installation

In SourceWeft

Open RCA-MCP in the dashboard and add it to a workspace.
Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

RCA-MCP Connector

[Python] [License] [PulseMCP]

Note: the api.rca-mcp.com custom domain isn't wired up yet — point RCA_MCP_API_URL at the current backend URL below instead.

What is RCA-MCP?

The only MCP server purpose-built for causal Root Cause Analysis. 56 tools covering causal graph construction, 10 RCA model families plus 3 dedicated PyRCA algorithms (Salesforce PyRCA, BSD-3-Clause), multi-model consensus, and PDF/HTML/Excel/Markdown report generation. Works with Claude, Ollama, Groq, OpenAI, Gemini, LangChain, Cursor — 10 providers.

Quick Start (2 minutes)

rca-mcp-connector is a published PyPI package — no clone needed. Point any MCP client at it with uvx (or pip install rca-mcp-connector if you'd rather manage the install yourself):

Get a free API key at rcamcp.datalizedglb.cloud — no credit card required — then set RCA_MCP_API_KEY in your MCP client's config (examples below).

Claude Code Setup

Add to .mcp.json in your workspace root:

json

{  "mcpServers": {    "rca-mcp": {      "command": "uvx",      "args": ["rca-mcp-connector"],      "env": {        "RCA_MCP_API_URL": "https://rcamcp-production.up.railway.app",        "RCA_MCP_API_KEY": "your_api_key_here"      }    }  }}

{  "mcpServers": {    "rca-mcp": {      "command": "uvx",      "args": ["rca-mcp-connector"],      "env": {        "RCA_MCP_API_URL": "https://rcamcp-production.up.railway.app",        "RCA_MCP_API_KEY": "your_api_key_here"      }    }  }}

Ollama Setup

bash

go install github.com/mark3labs/mcphost@latestmcphost -m ollama:qwen3:14b --config providers/mcp-servers.json

go install github.com/mark3labs/mcphost@latestmcphost -m ollama:qwen3:14b --config providers/mcp-servers.json

OpenAI Agents SDK

python

from agents import Agent, MCPServerStdioimport asyncio
async def main():    async with MCPServerStdio(        params={            "command": "uvx",            "args": ["rca-mcp-connector"],            "env": {                "RCA_MCP_API_URL": "https://rcamcp-production.up.railway.app",                "RCA_MCP_API_KEY": "your_api_key_here",            },        }    ) as rca_server:        agent = Agent(name="RCA Agent", model="gpt-4o", mcp_servers=[rca_server])        result = await agent.run("Find the root cause of the API latency spike.")        print(result.final_output)
asyncio.run(main())

from agents import Agent, MCPServerStdioimport asyncio
async def main():    async with MCPServerStdio(        params={            "command": "uvx",            "args": ["rca-mcp-connector"],            "env": {                "RCA_MCP_API_URL": "https://rcamcp-production.up.railway.app",                "RCA_MCP_API_KEY": "your_api_key_here",            },        }    ) as rca_server:        agent = Agent(name="RCA Agent", model="gpt-4o", mcp_servers=[rca_server])        result = await agent.run("Find the root cause of the API latency spike.")        print(result.final_output)
asyncio.run(main())

LangChain

python

from langchain_mcp_adapters.client import MultiServerMCPClientfrom langchain_anthropic import ChatAnthropicfrom langgraph.prebuilt import create_react_agentimport asyncio
async def main():    async with MultiServerMCPClient({        "rca-mcp": {            "command": "uvx", "args": ["rca-mcp-connector"],            "env": {                "RCA_MCP_API_URL": "https://rcamcp-production.up.railway.app",                "RCA_MCP_API_KEY": "your_api_key_here",            },            "transport": "stdio",        }    }) as client:        tools = await client.get_tools()        agent = create_react_agent(ChatAnthropic(model="claude-sonnet-4-6"), tools)        result = await agent.ainvoke({"messages": [{"role": "user", "content": "Run an FMEA analysis"}]})        print(result["messages"][-1].content)
asyncio.run(main())

from langchain_mcp_adapters.client import MultiServerMCPClientfrom langchain_anthropic import ChatAnthropicfrom langgraph.prebuilt import create_react_agentimport asyncio
async def main():    async with MultiServerMCPClient({        "rca-mcp": {            "command": "uvx", "args": ["rca-mcp-connector"],            "env": {                "RCA_MCP_API_URL": "https://rcamcp-production.up.railway.app",                "RCA_MCP_API_KEY": "your_api_key_here",            },            "transport": "stdio",        }    }) as client:        tools = await client.get_tools()        agent = create_react_agent(ChatAnthropic(model="claude-sonnet-4-6"), tools)        result = await agent.ainvoke({"messages": [{"role": "user", "content": "Run an FMEA analysis"}]})        print(result["messages"][-1].content)
asyncio.run(main())

See providers/ for ready-to-use config templates and full examples (Groq, Gemini, OpenRouter, Claude Desktop).

Third-Party Licences

Algorithms in rca_pyrca_* tools are independently-written adaptations of PyRCA's published methods (Zheng et al. 2023, arXiv:2306.11417), not direct copies of PyRCA source code, per the private API's models/pyrca_adapter.py.

Citing RCA-MCP

bibtex

@software{rcamcp2026,  title  = {RCA-MCP: An MCP Server for Causal Root Cause Analysis},  author = {dave1362},  year   = {2026},  url    = {https://github.com/dave1362/rca-mcp-connector},  note   = {v4.1.20}}

@software{rcamcp2026,  title  = {RCA-MCP: An MCP Server for Causal Root Cause Analysis},  author = {dave1362},  year   = {2026},  url    = {https://github.com/dave1362/rca-mcp-connector},  note   = {v4.1.20}}

Source: README.md at commit 25a2a64

Tools

SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

v4.1.20LatestSep 16, 2026

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