☁️

Azure Machine Learning MCP

Updated June 2026Trust grade A94/100

The Azure Machine Learning MCP, built by community, provides manage Azure ML experiments, datasets, compute clusters, model registries, and deployments through AI-assisted MLOps workflows. It is community-built and best for Cloud.

by community

About

Manage Azure ML experiments, datasets, compute clusters, model registries, and deployments through AI-assisted MLOps workflows.

A
Reliable94/100
low confidence · 1 measured signal

Grade A (94/100, reliable) from 1 measured signal, based on repository evidence. Only one signal stands behind it, so treat the grade as provisional.

What was measured

  • Repository maintenance100/100 · weight 20

    The repository has been pushed to or released within the last six months. — last push 2026-07-25, last release 2026-07-24 (azure-mgmt-containerservice_41.5.0).

  • Source verification100/100 · weight 25

    The repository URL was confirmed to resolve against the live GitHub API and is not archived.

  • Provenance65/100 · weight 10

    Community-built. That is not a mark against it — most of the ecosystem is community-built — but there is no vendor accountable for keeping it working.

  • Listing ↔ repository match100/100 · weight 5

    The listing name lines up with the linked repository Azure/azure-sdk-for-python.

What could not be measured

These contributed nothing to the score — not a penalty, not a zero. They are why the confidence reads the way it does.

  • Live MCP handshakeunknown

    No remote endpoint to handshake — this server installs and runs locally over stdio, so there is nothing to probe from the outside.

  • Measured uptimeunknown

    No probe history recorded for this server yet.

  • Tool-schema stabilityunknown

    Drift is a difference between two successive checks, and this server has none recorded.

Frequently Asked Questions

What is Azure Machine Learning MCP?
Azure Machine Learning MCP is an MCP server built by community. Manage Azure ML experiments, datasets, compute clusters, model registries, and deployments through AI-assisted MLOps workflows.
Who built Azure Machine Learning MCP?
Azure Machine Learning MCP was built by community.
Is Azure Machine Learning MCP free?
Yes, Azure Machine Learning MCP has a free option. This MCP server is free and open-source. Check the GitHub repository for details.
How do I install Azure Machine Learning MCP?
Install Azure Machine Learning MCP from its GitHub repository: https://github.com/Azure/azure-sdk-for-python
What does Azure Machine Learning MCP integrate with?
Azure Machine Learning MCP integrates with Claude Desktop, Cursor, VS Code.

Repo Health

Actively maintained

Local/stdio install — runs on your machine, so there is no remote endpoint to verify live. Trust signal below is from the source repo.

Last commit
1mo ago
Last release
azure-mgmt-containerservice_41.5.0 · 1mo ago
Install
pip

Quick Info

Install Type
pip
Author
community
Categories
3
Integrations
3

Related Servers

💻

Git

Tools to read, search, and manipulate Git repositories. Full Git operations support.

Local
🧠

Memory

Knowledge graph-based persistent memory system. Store and retrieve contextual information.

Local
🤖

Sequential Thinking MCP Server

a single structured-reasoning tool that lets a model plan, revise and branch its own chain of thought instead of answering in one shot. Published by Anthropic as part of the official modelcontextprotocol/servers monorepo (89,000+ stars, actively maintained), it exposes exactly one tool — sequential_thinking — and that tool is the whole product. Each call carries a `thought` string plus bookkeeping fields: `thoughtNumber`, `totalThoughts`, and `nextThoughtNeeded`, which the model flips to false when it is done. The interesting fields are the optional ones. `isRevision` and `revisesThought` let the model go back and correct an earlier step rather than plowing ahead on a bad assumption; `branchFromThought` and `branchId` let it fork into an alternative line of reasoning and carry both forward; `needsMoreThoughts` lets it extend past its own original estimate when a problem turns out to be deeper than it looked. In practice you never call the tool by hand. You connect the server to an MCP host and ask a question that deserves more than one pass — plan a PostgreSQL 14 to 16 migration and revise if downtime exceeds five minutes, work out why a deploy only fails in production, compare three architectures and branch when an assumption breaks. You can tell it is working when the host inspector shows repeated sequential_thinking calls with a rising `thoughtNumber` rather than a single response. Install with `npx -y @modelcontextprotocol/server-sequential-thinking` — note the hyphenated package name, which differs from both the `sequentialthinking` directory in the repo and the Docker image `mcp/sequentialthinking`, a mismatch that breaks a lot of copied configs. A Docker image is published alongside the npm package, and the README carries one-click VS Code install buttons for both transports. Set `DISABLE_THOUGHT_LOGGING=true` if you do not want every thought written to the server log.

Local
💻

GitHub MCP Server

authenticated access to the whole GitHub platform — repositories, files, branches, issues, pull requests, Actions runs, security alerts, discussions and notifications — from Claude, Cursor, VS Code, Copilot CLI and any other MCP host. There is no npm package for this server, and that trips up most people who try to install it: `@github/mcp-server` is not published to the npm registry, so any `npx` line you find for it will fail. GitHub ships it three other ways. The easiest is the hosted remote server at https://api.githubcopilot.com/mcp/, which needs no install at all — point an HTTP-transport MCP client at that URL and log in with OAuth (VS Code 1.101+, Claude Desktop, Claude Code, Cursor and Windsurf all support this). The second is the official Docker image ghcr.io/github/github-mcp-server, which is what the copy-paste command on this page runs; on github.com it now performs a browser-based OAuth login on first use and keeps the token in memory only, which is why the published Docker configs map a fixed loopback callback port (-p 127.0.0.1:8085:8085 with GITHUB_OAUTH_CALLBACK_PORT=8085) so the container can receive the callback. Prefer a token? Set GITHUB_PERSONAL_ACCESS_TOKEN instead — it takes precedence over OAuth, and the minimum useful scopes are repo, read:org and read:packages. The third is the native Go binary from the repository's releases, which needs no fixed port for the OAuth flow. GitHub Enterprise Server has no hosted option: use the local server with --gh-host or GITHUB_HOST set to your instance (include the https:// scheme — it defaults to http://, which GHES rejects). Toolsets can be narrowed with GITHUB_TOOLSETS, and an insiders channel is available at /mcp/insiders or via the X-MCP-Insiders header.

Auth required📘
💻

GitLab MCP Server

a first-party MCP endpoint built into the GitLab instance itself — there is no package to install, because the server ships inside GitLab and answers at https://<your-gitlab>/api/v4/mcp (gitlab.com exposes the same path, so https://gitlab.com/api/v4/mcp works for SaaS projects). It landed as an experiment in GitLab 18.3 and moved to beta in 18.6. Authentication is the part that makes it different from every community GitLab server: it uses OAuth 2.0 Dynamic Client Registration, so the first time a client connects it registers itself as an OAuth application on your instance and is issued an access token — no personal access token pasted into a config file. Administrators who do not want one OAuth application per tool can pre-create a shared application instead. Three prerequisites are what actually block most first connections: GitLab Duo must be set to Always on or On by default, beta and experimental features must be enabled, and MCP access must be switched on at the group or instance level. The tool surface covers issues and merge requests (create_issue, get_issue, create_merge_request, get_merge_request, list_merge_requests, get_merge_request_commits, get_merge_request_diffs, get_merge_request_pipelines, create_merge_request_note, get_merge_request_notes), CI/CD (manage_pipeline for list/create/delete/retry/cancel, get_pipeline_jobs, get_job_log), work items (create_workitem_note, get_workitem_notes, link_work_items, get_saved_view_work_items), search (search across the instance, search_labels, semantic_code_search), list_wiki_pages, and attach_scan_profile. HTTP is the recommended transport — claude mcp add --transport http GitLab https://gitlab.com/api/v4/mcp — and clients that only speak stdio can wrap it with npx mcp-remote <url> on Node 20+. Send the X-Gitlab-Mcp-Server-Tool-Name-Prefix header if generic names like search collide with another connected server. If your instance predates 18.3 or Duo is not available to you, the community alternative most teams land on is zereight/gitlab-mcp (1,889 stars as of 2026-08-16, npm @zereight/mcp-gitlab), which authenticates with a plain personal access token and ships 217 tools — including merge_merge_request, approve_merge_request, execute_graphql and full CI/CD variable management, none of which the built-in server exposes — behind GITLAB_PERMISSION_MODE=readonly/modify and GITLAB_TOOLSETS/GITLAB_TOOLS filtering. One further change worth noting: MCP server access moved from GitLab Premium to GitLab Free in 19.2 and became a setting of its own.

Auth required📘

Sponsored

Better Stack

Free Plan

Get alerted when your APIs, browser tests, payment pipelines, or MCP server dependencies go down. Used by 100K+ developers.

Start monitoring free →