Rootly MCP-Server

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.

Demo GIF

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
Intelligent Incident Analysis: Smart tools that analyze 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

Available Tools

Alerts

listIncidentAlerts
listAlerts
attachAlert
createAlert

Environments

listEnvironments
createEnvironment

Functionalities

listFunctionalities
createFunctionality

Workflows

listWorkflows
createWorkflow

Incidents

listIncidentActionItems
createIncidentActionItem
listIncident_Types
createIncidentType
search_incidents
find_related_incidents
suggest_solutions

Services & Severities

listServices
createService
listSeverities
createSeverity

Teams & Users

listTeams
createTeam
listUsers
getCurrentUser

Endpoints

list_endpoints

Why Path Limiting?

We limit exposed API paths for two key reasons:

Context Management: Rootly's comprehensive API can overwhelm AI agents, affecting their ability to perform simple tasks effectively
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.

Smart Analysis Tools

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

`find_related_incidents`

Finds historically similar incidents using text similarity 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 intelligent 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. Rootly AI logo

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.

Deploy Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

View all tools

Verwalten Sie Vorfälle von Ihrer IDE aus. Ein MCP-Server ermöglicht das Abrufen von Vorfällen und den zugehörigen Metadaten mithilfe der Rootly-API.

Related Resources

Reddit Discussion about this server

Related MCP Servers

MCP Server Linear
floodfx
A
security
A
license
A
quality
An MCP server that provides tools for interacting with Linear issue tracking system, currently supporting issue search with plans to expand to issue creation, updates, comments, and project management.
Last updated -
14
1
MIT License
EventCatalog MCP Serverofficial
event-catalog
-
security
F
license
-
quality
A server that connects MCP clients (Claude, Cursor, Windsurf, etc.) to EventCatalog instances, allowing users to ask questions about their event-driven architecture and get insights about domains, services, and message schemas directly within their development tools.
Last updated -
14
16
Sentry MCP Server
javaDer
-
security
F
license
-
quality
An MCP server that connects to Sentry.io or self-hosted Sentry instances to retrieve and analyze error reports, stack traces, and debugging information.
Last updated -
1
Rootly MCP Server for Cloudflare Workers
Rootly-AI-Labs
-
security
F
license
-
quality
A remote MCP server that provides AI agents access to the Rootly API for incident management, allowing users to query and manage incidents, alerts, teams, services, and other incident management resources through natural language.
Last updated -

View all related MCP servers

Rootly MCP server