RCA-MCP Connector

RCA-MCP Connector

Enables causal root cause analysis with 55 tools covering causal graph construction, 13 RCA model families, multi-model consensus, and report generation. Works with 9 LLM providers including Claude, Ollama, and OpenAI.

Category
访问服务器

README

RCA-MCP Connector

Python License PulseMCP

What is RCA-MCP?

The only MCP server purpose-built for causal Root Cause Analysis. 55 tools covering causal graph construction, 13 RCA model families (including Salesforce PyRCA algorithms), multi-model consensus, and PDF/HTML/Excel/Markdown report generation. Works with Claude, Ollama, Groq, OpenAI, Gemini, LangChain — 9 providers.


Quick Start (2 minutes)

git clone https://github.com/dave1362/rca-mcp-connector.git
cd rca-mcp-connector
pip install -r requirements.txt
cp .env.example .env
# Edit .env: set RCA_MCP_API_KEY to your key from https://rca-mcp.com/upgrade

Get an API key at rca-mcp.com — a free tier is available, no credit card required.


Claude Code Setup

Add to .mcp.json in your workspace root:

{
  "mcpServers": {
    "rca-mcp": {
      "command": "python",
      "args": ["/absolute/path/to/rca-mcp-connector/connector/server.py"],
      "env": {
        "RCA_MCP_API_URL": "https://api.rca-mcp.com",
        "RCA_MCP_API_KEY": "your_api_key_here"
      }
    }
  }
}

Ollama Setup

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

OpenAI Agents SDK

from agents import Agent, MCPServerStdio
import asyncio

async def main():
    async with MCPServerStdio(
        params={
            "command": "python",
            "args": ["connector/server.py"],
            "env": {"RCA_MCP_API_KEY": "your_key"},
        }
    ) 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

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
import asyncio

async def main():
    async with MultiServerMCPClient({
        "rca-mcp": {
            "command": "python", "args": ["connector/server.py"],
            "env": {"RCA_MCP_API_KEY": "your_key"}, "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).


All 55 Tools

See rca-mcp.com/pricing for full tier details and limits.

Legend: ✅ All plans · 🌟 Starter+ · 💎 Pro+ · 👑 Enterprise only

Group A — Causal Graph

Tool Plan Description
rca_graph_create Create an empty causal DAG
rca_graph_get Retrieve a graph as JSON / DOT / adjacency
rca_graph_score Structural quality scoring
rca_graph_discover 💎 Auto-discover causal structure from data (PC-algorithm)
rca_graph_merge 💎 Merge two graphs for cross-system RCA
rca_graph_list_versions 🌟 List historical graph snapshots
rca_graph_restore_version 👑 Roll back to a historical version (admin)
rca_graph_delete 👑 Delete a graph permanently (admin)

Group B — RCA Models

Tool Plan Description
rca_model_create Register a model spec (up to plan's model limit)
rca_model_list List registered models
rca_model_update_status Advance model lifecycle
rca_model_validate Validate on hold-out data
rca_model_delete 👑 Delete a model (admin)

Group C — Analysis

Tool Plan Description
rca_analysis_run Run RCA, get ranked root causes
rca_analysis_run_async 💎 Submit a long-running analysis (returns task_id)
rca_analysis_poll_task 💎 Poll an async task's status
rca_analysis_ensemble 💎 Combine 2-5 models via weighted-vote consensus
rca_analysis_get_result Retrieve a stored result
rca_analysis_list_results Paginated result listing
rca_analysis_query_results Filter results by family/confidence/tags
rca_analysis_compare Cross-model consensus comparison
rca_analysis_explain Narrative explanation of a result
rca_analysis_batch 🌟 Batch analysis (5 incidents Starter, 20 Pro+)

Group D — Graph Operations

Tool Plan Description
rca_graph_add_node Add a typed node
rca_graph_remove_node Remove a node
rca_graph_add_edge Add a directed causal edge (cycle-safe)
rca_graph_remove_edge Remove an edge
rca_graph_score_paths Rank causal paths to a target node
rca_graph_markov_blanket Get parents/children/co-parents of a node

Group E — Admin & Auth

Tool Plan Description
rca_auth_generate_token Generate API key + JWT — call this first
rca_auth_revoke_token 👑 Invalidate a JWT before expiry (admin)
rca_auth_list_keys 👑 List API keys (admin + Pro+ multi-key feature)
rca_auth_rotate_key 👑 Rotate an API key (admin + Pro+ multi-key feature)
rca_admin_health Server health snapshot
rca_admin_read_audit_log Read structured audit entries
rca_admin_purge_namespace 👑 Delete all records in a namespace (admin)
rca_admin_show_plan_info Show current plan, limits, and upgrade options

Group F — Reports & Providers

Tool Plan Description
rca_report_generate ✅/🌟/💎 Markdown all plans; HTML/PDF Starter+; Excel Pro+
rca_report_compare ✅/🌟 Comparative report; Markdown all, HTML Starter+
rca_provider_list_configs Setup instructions for any of the 9 providers

Group G — PyRCA Algorithms [PyRCA — BSD-3-Clause]

Tool Plan Description
rca_pyrca_epsilon_diagnosis 🌟 z-score anomalous metric identification
rca_pyrca_random_walk 🌟 Personalised PageRank root cause localisation
rca_pyrca_ht_diagnosis 🌟 Hypothesis testing with descendant adjustment
rca_pyrca_validate_setup Check PyRCA strategy and licence compliance

Group H — Equipment Knowledge (Phase 9)

Tool Plan Description
rca_guide_ingest 🌟 Ingest a troubleshooting guide (markdown/plain/json_dtree)
rca_guide_search Search guides by symptom (TF-IDF); Free capped at 3 results
rca_guide_get Retrieve a full guide or a specific section
rca_guide_list List ingested guides with equipment_type/tag filters
rca_dtree_start 🌟 Begin an interactive diagnostic decision-tree session
rca_dtree_answer 🌟 Answer a diagnostic question; returns next question or diagnosis
rca_dtree_list_sessions 🌟 List diagnostic sessions with equipment/status filters
rca_guide_generate_report 🌟/💎 Maintenance report from a resolved session; Markdown Starter+, PDF/HTML Pro+
rca_dtree_generate_from_fmea 🌟 Auto-generate a decision tree from FMEA results
rca_guide_pdf_preview 🌟 Preview PDF quality before ingestion
rca_guide_ingest_pdf 🌟 Parse a PDF equipment manual → knowledge base

Ships with 4 built-in sample guides (pump, vacuum interface valve, ML pipeline, CFD solver) auto-loaded on first startup, and supports 26 equipment types. PDF ingestion supports 4 parsing strategies (text_native, ocr, table, mixed).


Get an API Key

rca-mcp.com/upgrade — Free tier available, plans from $19/month (Starter), $79/month (Pro), $399/month (Enterprise).


Third-Party Licences

PyRCA (Salesforce): BSD-3-Clause Copyright (c) 2022, salesforce.com, inc. https://github.com/salesforce/PyRCA

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

@software{rcamcp2026,
  title  = {RCA-MCP: An MCP Server for Causal Root Cause Analysis},
  author = {davetj},
  year   = {2026},
  url    = {https://github.com/dave1362/rca-mcp-connector},
  note   = {v3.0.0}
}

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选