The Rootly MCP server integrates with MCP-compatible IDEs to streamline incident resolution by providing access to Rootly's API endpoints. It's optimized for AI agents and enables management of various Rootly resources directly from your IDE.
Key capabilities:
Incident Management: Create, list, and search incidents with advanced pagination controls
Alert Handling: Create, list, and attach alerts to incidents
Resource Management: Create and list environments, functionalities, workflows, incident types, services, severities, and teams
User Operations: List all users and retrieve current user details
Action Items: Create and manage incident action items
API Discovery: List all available Rootly API endpoints
IDE Integration: Designed for direct interaction with Rootly from editors like Cursor and Windsurf
AI Optimization: Features dynamic tool generation from OpenAPI specifications with smart pagination to manage context windows
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- 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
With uvx
To customize allowed_paths
and access additional Rootly API paths, clone the repository and use this configuration:
Connect to Hosted MCP Server
Alternatively, connect directly to our hosted MCP server:
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 incidentssuggest_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
):
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
.
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:
- 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:
- 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:
2. Install Dependencies
Install all project dependencies:
To add new dependencies during development:
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.
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
Manage incidents from your IDE. An MCP server allowing to pull out incidents and their associated metadata using Rootly API.
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