Rootly MCP server

# Rootly MCP Server [](https://pypi.org/project/rootly-mcp-server/) [](https://pypi.org/project/rootly-mcp-server/) [](https://pypi.org/project/rootly-mcp-server/) An MCP server for the [Rootly API](https://docs.rootly.com/api-reference/overview) that integrates seamlessly with MCP-compatible editors like Cursor, Windsurf, and Claude. Resolve production incidents in under a minute without leaving your IDE. [](https://cursor.com/install-mcp?name=rootly&config=eyJjb21tYW5kIjoibnB4IC15IG1jcC1yZW1vdGUgaHR0cHM6Ly9tY3Aucm9vdGx5LmNvbS9zc2UgLS1oZWFkZXIgQXV0aG9yaXphdGlvbjoke1JPT1RMWV9BVVRIX0hFQURFUn0iLCJlbnYiOnsiUk9PVExZX0FVVEhfSEVBREVSIjoiQmVhcmVyIDxZT1VSX1JPT1RMWV9BUElfVE9LRU4%2BIn19)  ## Prerequisites - Python 3.12 or higher - `uv` package manager ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` - [Rootly API token](https://docs.rootly.com/api-reference/overview#how-to-generate-an-api-key%3F) ## 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 ```json { "mcpServers": { "rootly": { "command": "uv", "args": [ "tool", "run", "--from", "rootly-mcp-server", "rootly-mcp-server", ], "env": { "ROOTLY_API_TOKEN": "<YOUR_ROOTLY_API_TOKEN>" } } } } ``` ### With uvx ```json { "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: ```json { "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: ```json { "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: 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`. ### 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=0.15, 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](https://labs.rootly.ai/), 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`](https://github.com/astral-sh/uv) for dependency management ### 1. Set Up Virtual Environment Create and activate a virtual environment: ```bash uv venv .venv source .venv/bin/activate # Always activate before running scripts ``` ### 2. Install Dependencies Install all project dependencies: ```bash uv pip install . ``` To add new dependencies during development: ```bash 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.