Rootly Mcp 伺服器

Rootly MCP 伺服器是什麼？

Rootly MCP 伺服器是一個創新的平台，旨在簡化和增強雲資源和服務的管理。它為開發人員和 IT 團隊提供了一個集中式的中心，以高效地監控、控制和優化他們的雲基礎設施。憑藉其用戶友好的界面和強大的功能，Rootly MCP 伺服器簡化了複雜的雲操作，使各種規模的團隊都能輕鬆使用。

Rootly MCP 伺服器的功能

• 集中管理：從單一儀表板管理所有雲資源，便於監督和控制。
• 實時監控：即時獲取雲服務的性能和健康狀況更新，實現主動管理。
• 自動警報：設置關鍵事件的通知，確保您的團隊始終了解重要變更或問題。
• 可擴展性：根據項目需求輕鬆調整雲資源的規模，優化成本和性能。
• 用戶友好界面：以可用性為設計理念，該平台使用戶能夠輕鬆導航和管理資源。
• 集成能力：與各種第三方工具和服務無縫集成，以增強功能並簡化工作流程。

如何使用 Rootly MCP 伺服器

1. 註冊：在 Rootly MCP 伺服器平台上創建一個帳戶。
2. 連接您的雲服務：將現有的雲帳戶鏈接到 Rootly MCP 伺服器以進行集中管理。
3. 配置設置：根據團隊的需求自定義儀表板並設置警報。
4. 監控性能：使用實時監控工具跟踪您的雲資源和性能指標。
5. 優化資源：分析使用模式並調整資源，以確保最佳性能和成本效益。

常見問題

問：我可以使用 Rootly MCP 伺服器管理哪些類型的雲服務？

答：Rootly MCP 伺服器支持各種雲服務，包括 AWS、Azure 和 Google Cloud，允許您在一個地方管理來自不同提供商的各種資源。

問：是否提供免費試用？

答：是的，Rootly MCP 伺服器為新用戶提供免費試用，以便在訂閱之前探索其功能和能力。

問：我可以將 Rootly MCP 伺服器與其他工具集成嗎？

答：當然可以！Rootly MCP 伺服器支持與眾多第三方應用程序的集成，增強其功能並實現更流暢的工作流程。

問：Rootly MCP 伺服器如何確保數據安全？

答：Rootly MCP 伺服器採用行業標準的安全措施，包括加密和安全訪問協議，以保護您的數據並確保遵守法規。

問：有哪些支持選項可用？

答：Rootly MCP 伺服器提供多種支持選項，包括文檔、社區論壇和直接客戶支持，以解答任何查詢或問題。

Rootly MCP Server

An MCP server for the Rootly API that integrates seamlessly with MCP-compatible editors like Cursor, Windsurf, and Claude. Resolve production incidents in under a minute without leaving your IDE.

Prerequisites

• Python 3.12 or higher
• uv package manager

  curl -LsSf https://astral.sh/uv/install.sh | sh
  

• Rootly API token

Installation

Configure your MCP-compatible editor (tested with Cursor) with one of the configurations below. The package will be automatically downloaded and installed when you first open your editor.

With uv

{
  "mcpServers": {
    "rootly": {
      "command": "uv",
      "args": [
        "tool",
        "run",
        "--from",
        "rootly-mcp-server",
        "rootly-mcp-server",
      ],      
      "env": {
        "ROOTLY_API_TOKEN": "<YOUR_ROOTLY_API_TOKEN>"
      }
    }
  }
}

With uvx

{
  "mcpServers": {
    "rootly": {
      "command": "uvx",
      "args": [
        "--from",
        "rootly-mcp-server",
        "rootly-mcp-server",
      ],      
      "env": {
        "ROOTLY_API_TOKEN": "<YOUR_ROOTLY_API_TOKEN>"
      }
    }
  }
}

To customize allowed_paths and access additional Rootly API paths, clone the repository and use this configuration:

{
  "mcpServers": {
    "rootly": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/rootly-mcp-server",
        "rootly-mcp-server"
      ],
      "env": {
        "ROOTLY_API_TOKEN": "<YOUR_ROOTLY_API_TOKEN>"
      }
    }
  }
}

Connect to Hosted MCP Server

Alternatively, connect directly to our hosted MCP server:

{
  "mcpServers": {
    "rootly": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.rootly.com/sse",
        "--header",
        "Authorization:${ROOTLY_AUTH_HEADER}"
      ],
      "env": {
        "ROOTLY_AUTH_HEADER": "Bearer <YOUR_ROOTLY_API_TOKEN>"
      }
    }
  }
}

Features

• Dynamic Tool Generation: Automatically creates MCP resources from Rootly's OpenAPI (Swagger) specification
• Smart Pagination: Defaults to 10 items per request for incident endpoints to prevent context window overflow
• API Filtering: Limits exposed API endpoints for security and performance
• AI-Powered Incident Analysis: Smart tools that learn from historical incident data
  • find_related_incidents: Uses TF-IDF similarity analysis to find historically similar incidents
  • suggest_solutions: Mines past incident resolutions to recommend actionable solutions
• MCP Resources: Exposes incident and team data as structured resources for easy AI reference
• Intelligent Pattern Recognition: Automatically identifies services, error types, and resolution patterns

Whitelisted Endpoints

By default, the following Rootly API endpoints are exposed to the AI agent (see allowed_paths in src/rootly_mcp_server/server.py):

/v1/incidents
/v1/incidents/{incident_id}/alerts
/v1/alerts
/v1/alerts/{alert_id}
/v1/severities
/v1/severities/{severity_id}
/v1/teams
/v1/teams/{team_id}
/v1/services
/v1/services/{service_id}
/v1/functionalities
/v1/functionalities/{functionality_id}
/v1/incident_types
/v1/incident_types/{incident_type_id}
/v1/incident_action_items
/v1/incident_action_items/{incident_action_item_id}
/v1/incidents/{incident_id}/action_items
/v1/workflows
/v1/workflows/{workflow_id}
/v1/workflow_runs
/v1/workflow_runs/{workflow_run_id}
/v1/environments
/v1/environments/{environment_id}
/v1/users
/v1/users/{user_id}
/v1/users/me
/v1/status_pages
/v1/status_pages/{status_page_id}

Why Path Limiting?

We limit exposed API paths for two key reasons:

1. Context Management: Rootly's comprehensive API can overwhelm AI agents, affecting their ability to perform simple tasks effectively
2. Security: Controls which information and actions are accessible through the MCP server

To expose additional paths, modify the allowed_paths variable in src/rootly_mcp_server/server.py.

AI-Powered Smart Tools

The MCP server includes intelligent tools that analyze historical incident data to provide actionable insights:

find_related_incidents

Finds historically similar incidents using machine learning text analysis:

find_related_incidents(incident_id="12345", similarity_threshold=0.3, max_results=5)

• Input: Incident ID, similarity threshold (0.0-1.0), max results
• Output: Similar incidents with confidence scores, matched services, and resolution times
• Use Case: Get context from past incidents to understand patterns and solutions

suggest_solutions

Recommends solutions by analyzing how similar incidents were resolved:

suggest_solutions(incident_id="12345", max_solutions=3)
### OR for new incidents:
suggest_solutions(incident_title="Payment API errors", incident_description="Users getting 500 errors during checkout")

• Input: Either incident ID OR title/description text
• Output: Actionable solution recommendations with confidence scores and time estimates
• Use Case: Get AI-powered suggestions based on successful past resolutions

How It Works

• Text Similarity: Uses TF-IDF vectorization and cosine similarity (scikit-learn)
• Service Detection: Automatically identifies affected services from incident text
• Pattern Recognition: Finds common error types, resolution patterns, and time estimates
• Fallback Mode: Works without ML libraries using keyword-based similarity
• Solution Mining: Extracts actionable steps from resolution summaries

Data Requirements

For optimal results, ensure your Rootly incidents have descriptive:

• Titles: Clear, specific incident descriptions
• Summaries: Detailed resolution steps when closing incidents
• Service Tags: Proper service identification

Example good resolution summary: "Restarted auth-service, cleared Redis cache, and increased connection pool from 10 to 50"

About Rootly AI Labs

This project was developed by Rootly AI Labs, where we're building the future of system reliability and operational excellence. As an open-source incubator, we share ideas, experiment, and rapidly prototype solutions that benefit the entire community.

Developer Setup & Troubleshooting

Prerequisites

• Python 3.12 or higher
• uv for dependency management

1. Set Up Virtual Environment

Create and activate a virtual environment:

uv venv .venv
source .venv/bin/activate  # Always activate before running scripts

2. Install Dependencies

Install all project dependencies:

uv pip install .

To add new dependencies during development:

uv pip install <package>

3. Verify Installation

The server should now be ready to use with your MCP-compatible editor.

For developers: Additional testing tools are available in the tests/ directory.