Developer tools · MCP server
Dynatrace managed MCP: MCP server, setup and security
Dynatrace managed MCP is an open-source MCP server published on GitHub that lets an AI agent query problems, logs and performance metrics from self-hosted Dynatrace Managed environments. Its repository has 32 stars and its last commit is from October 21, 2026 and it is licensed under Apache-2.0.
| Adoption (stars) | 15/40 | |
|---|---|---|
| Maintenance | 30/30 | |
| Framework (licence, registry, official) | 30/30 |
What Dynatrace managed MCP does
According to its repository: “Query problems, logs, events, SLOs, security insights and performance metrics from self-hosted Dynatrace Managed environments.”
The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. Dynatrace managed MCP is listed under Developer tools, a category of 475 servers in the directory, where it ranks #298 by stars.
Topics declared by the repository: dynatrace, managed, monitoring, observability.
Installing and connecting Dynatrace managed MCP
Distribution modes declared in the official MCP registry:
- npm package (Node.js, usually started with
npx)
It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: TypeScript. Listed systems: macOS, Windows, Linux.
Security: what to check
Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.
None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Dynatrace managed MCP. That does not replace reading the code.
Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.
Project activity and maintenance
Created on October 27, 2025, the dynatrace-oss/dynatrace-managed-mcp repository has 32 stars and 14 forks, with 15 open issues. It has more stars than 45% of the servers in the directory. It received changes this month. Version published in the registry: 1.1.1 (September 1, 2026).
Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.
Verdict
Dynatrace managed MCP is one of the most solid servers in its category: followed, maintained and well framed.
Strengths
- Maintained by the service's own vendor
- Active project: recent commit
- Listed in the official MCP registry
- Permissive licence (Apache-2.0)
Alternatives
- Radar Kubernetes MCP Server3.9K ★
- Repowise7.9K ★
- Jscpd6.4K ★
- Apify MCP server12.9K ★
- Codebase Memory47.4K ★
- PostHog MCP Server40.4K ★
Learn more
Frequently asked questions
How do I install Dynatrace managed MCP?
Dynatrace managed MCP is distributed as npm package (Node.js, usually started with npx). Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of dynatrace-oss/dynatrace-managed-mcp.
Is Dynatrace managed MCP free?
The code is published under the Apache-2.0 licence and can be used free of charge.
Is Dynatrace managed MCP safe?
No risk signal found (system access, secrets, licence, abandonment). As with any MCP server, keep rights minimal and read the code before sensitive use.
Is it an official server?
Yes: the awesome-mcp-servers list marks it as the service vendor's official implementation.
What are the alternatives to Dynatrace managed MCP?
In the same category: Radar Kubernetes MCP Server, Repowise, Jscpd, and Apify MCP server. Radar Kubernetes MCP Server and Repowise score higher on our grid.
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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score