zenml-io/mcp-zenml
by Various
MCP server to connect an MCP client (Cursor, Claude Desktop etc) with your ZenML MLOps and LLMOps pipelines
MCP
zenml-io/mcp-zenml
Added 1 June 2026
Overview
An MCP server that acts as a bridge between MCP-compatible clients (such as Cursor and Claude Desktop) and ZenML MLOps and LLMOps pipelines. It enables these clients to interact with pipeline runs, artifacts, and deployments through the Model Context Protocol.
Best for
Best for
ZenML users who want to interact with their MLOps and LLMOps pipelines through MCP-enabled tools and AI assistants
Use cases
- Query pipeline status and logs from an AI assistant inside a code editor
- Trigger ZenML pipeline runs or deployments via natural language commands
- Retrieve artifact metadata and model versions through a chat interface
Notes
An MCP server that acts as a bridge between MCP-compatible clients (such as Cursor and Claude Desktop) and ZenML MLOps and LLMOps pipelines. It enables these clients to interact with pipeline runs, artifacts, and deployments through the Model Context Protocol.
45 stars on GitHub. Last updated 2026-06-01. Licensed MIT.
Use cases
- Query pipeline status and logs from an AI assistant inside a code editor
- Trigger ZenML pipeline runs or deployments via natural language commands
- Retrieve artifact metadata and model versions through a chat interface
Pros
- Native integration with ZenML’s MLOps and LLMOps workflows
- Works with popular MCP clients like Cursor and Claude Desktop
- Open source and written in Python, easy to extend or customize
Cons
- Low GitHub star count (45) suggests limited adoption or early development stage
- Requires a running ZenML server and configured pipelines to be useful
- Documentation and community support may be sparse compared to larger projects
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Native integration with ZenML's MLOps and LLMOps workflows
- Works with popular MCP clients like Cursor and Claude Desktop
- Open source and written in Python, easy to extend or customize
Cons
- Low GitHub star count (45) suggests limited adoption or early development stage
- Requires a running ZenML server and configured pipelines to be useful
- Documentation and community support may be sparse compared to larger projects
Pairs with
Other entries in the index that connect to this one. Click through to see the chain.