Rootly Mcp 服务器

什么是 Rootly MCP 服务器？

Rootly MCP 服务器是一个创新的平台，旨在简化和增强云资源和服务的管理。它为开发人员和 IT 团队提供了一个集中管理的中心，以高效地监控、控制和优化他们的云基础设施。凭借其用户友好的界面和强大的功能，Rootly MCP 服务器简化了复杂的云操作，使各类团队都能轻松使用。

Rootly MCP 服务器的特点

• 集中管理：从单一仪表板管理所有云资源，便于监督和控制。
• 实时监控：即时获取云服务的性能和健康状态更新，便于主动管理。
• 自动警报：设置关键事件的通知，确保您的团队始终了解重要变化或问题。
• 可扩展性：根据项目需求轻松调整云资源的规模，优化成本和性能。
• 用户友好的界面：以可用性为设计理念，平台允许用户轻松导航和管理资源。
• 集成能力：与各种第三方工具和服务无缝集成，以增强功能并简化工作流程。

如何使用 Rootly MCP 服务器

1. 注册：在 Rootly MCP 服务器平台上创建一个帐户。
2. 连接您的云服务：将现有的云帐户链接到 Rootly MCP 服务器以实现集中管理。
3. 配置设置：根据团队的需求自定义仪表板并设置警报。
4. 监控性能：使用实时监控工具跟踪您的云资源和性能指标。
5. 优化资源：分析使用模式并调整资源，以确保最佳性能和成本效益。

常见问题解答

问：我可以使用 Rootly MCP 服务器管理哪些类型的云服务？

答：Rootly MCP 服务器支持广泛的云服务，包括 AWS、Azure 和 Google Cloud，允许您在一个地方管理来自不同提供商的各种资源。

问：是否提供免费试用？

答：是的，Rootly MCP 服务器为新用户提供免费试用，以便在订阅之前探索其功能和能力。

问：我可以将 Rootly MCP 服务器与其他工具集成吗？

答：当然可以！Rootly MCP 服务器支持与众多第三方应用程序的集成，增强其功能并允许更流畅的工作流程。

问：Rootly MCP 服务器如何确保数据安全？

答：Rootly MCP 服务器采用行业标准的安全措施，包括加密和安全访问协议，以保护您的数据并确保遵守法规。

问：有哪些支持选项可用？

答：Rootly MCP 服务器提供多种支持选项，包括文档、社区论坛和直接客户支持，以解答任何询问或问题。

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.