dynatrace-oss/dynatrace-managed-mcp

Use cases
> 1. Your Dynatrace Managed environment(s) is/are the primary Observability system, containing all live data; or
> 2. There has been a migration from a Dynatrace Managed environment to a Dynatrace Saas environment; however, historical observability data has not been migrated and can still be accessed via a Dynatrace Managed environment.
> The Dynatrace Managed MCP is used to access historical data, and a separate Dynatrace SaaS MCP is used to access live and more recent data.
> Specific use cases for the Dynatrace Managed MCP include:
>
> - **Real-time observability** - Fetch production-level data for early detection and proactive monitoring
> - **Contextual debugging** - Fix issues with full context from monitored exceptions, logs, and anomalies
> - **Security insights** - Get detailed vulnerability analysis and security problem tracking. This can include multicloud compliance assessment with evidence-based investigation.
> - **Natural language queries** - Queries are mapped to MCP tool usage, and thus API queries, with guidance for the next step
> - **Multiphase incident investigation** - Systematic impact assessment and troubleshooting
> - **Multienvironment support** - Query multiple Dynatrace Managed environments from the same MCP server
Capabilities
> - **Problems** - List and get problem details from your services (for example Kubernetes)
> - **Security** - List and get security problems / vulnerability details
> - **Entities** - Get more information about a monitored entity, including relationship mappings
> - **SLO** - List and get Service Level Objective details, including evaluation and error budgets
> - **Event Tracking** - List and get system events
> - **Log Investigation** - Search and filter logs with advanced content and time-based queries
> - **Metrics Analysis** - Query and analyze performance metrics using V2 Metrics API
The local _Dynatrace Managed MCP server_ allows AI Assistants to interact with one or more self-hosted Dynatrace Managed deployments, bringing observability data directly into your AI-assisted workflow.
This MCP server supports **two modes**:
- **Local mode:** Runs on your machine for development and testing.
- **Remote mode:** Connects over HTTP/SSE for distributed or production-like setups.
> [!TIP]
> This MCP server is specifically designed for Dynatrace Managed (self-hosted) deployments.
> For Dynatrace SaaS environments, please use the Dynatrace MCP.
> [!NOTE]
> This open source product is supported by the community.
> For feature requests, questions, or assistance, please use GitHub Issues.
Quickstart in stdio (local) mode
You can add this MCP server to your AI Assistant, such as VSCode, Claude, Cursor, Kiro, Windsurf, ChatGPT, or GitHub Copilot.
To run this MCP server, you to have to configure four things:
- _**Dynatrace Managed API token**_
- _**Configuration file**_: dt-config.yaml or dt-config.json file which is responsible for defining list of environments you intend to use
- _**MCP Server connection configuration file:**_ local mcp configuration, which is dependent on tools you are using
- Set DT_CONFIG_FILE to the path of your dt-config.yaml or dt-config.json file in the MCP server environment.
Dynatrace Managed API token
For information about creating API tokens in Managed deployments, refer to the Dynatrace Managed documentation.
Your API token must include the following scopes for full functionality:
- Access problem and event feed, metrics, and topology (DataExport)
- Read entities (entities.read)
- Read events (events.read)
- Read logs (logs.read)
- Read metrics (metrics.read)
- Read problems (problems.read)
- Read security problems (securityProblems.read)
- Read SLO (slo.read)
Configuration File
Configuration parameters
| Parameter | Required | Description | Example value |
| -------------- | ------------------ | --------------------------------------------------------------------------------- | ------------------------------------------------- |