Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@pipen-mcp list available bioinformatics pipeline namespaces"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
pipen-mcp bridges pipen's process/pipeline ecosystem with AI assistants that support the Model Context Protocol. Once installed, any namespace registered via the pipen_cli_run entry point group is automatically discoverable and executable by an MCP-compatible client (e.g., Claude, VS Code Copilot, Cursor).
Related MCP server: NGS360 MCP Server
pip install pipen-mcppipen-mcp requires Python ≥ 3.10 and depends on:
pipen-cli-run ≥ 1.0.1
pipen-annotate ≥ 1.0
mcp[cli] ≥ 2.0
pipen-mcp adds an mcp subcommand to the pipen CLI:
pipen mcp [--transport {stdio,sse,streamable-http}] [--host HOST] [--port PORT]Option | Description | Default |
| MCP transport ( |
|
| Host to bind to (SSE / streamable-http only) |
|
| Port to listen on (SSE / streamable-http only) |
|
Suitable for direct integration with MCP clients that launch the server as a subprocess:
pipen mcpStarts an HTTP server with Server-Sent Events transport:
pipen mcp --transport sse --host 0.0.0.0 --port 8520Starts an HTTP server with the streamable-HTTP transport:
pipen mcp --transport streamable-http --host 0.0.0.0 --port 8520The server exposes four tools that support a progressive-disclosure workflow:
Tool | Description |
| List all available namespaces. Start here to discover what is installed. |
| List all processes/pipelines available in a namespace. |
| Get the full argument schema for a specific process/pipeline. |
| Execute a process/pipeline with a list of CLI arguments. |
1. get_namespaces()
→ "delim", "bam", "rnaseq", ...
2. get_processes("delim")
→ RowsBinder (proc): Bind rows of input files
→ ColsBinder (proc): Bind columns of input files
3. get_process("delim", "RowsBinder")
→ Required:
--in.infiles <list[str]> Input files
→ Optional:
--envs.sep <str> (default: '\t') Separator
--outdir <str> Output directory
...
4. run_process("delim", "RowsBinder", [
"--in.infiles", "/tmp/a.csv,/tmp/b.csv",
"--envs.sep", ",",
"--outdir", "/tmp/out"
])
→ Pipeline output / logsAdd the server to your MCP configuration (~/.vscode/mcp.json or ~/.vscode-server/data/User/mcp.json):
{
"servers": {
"pipen-mcp": {
"type": "stdio",
"command": "pipen",
"args": ["mcp"]
}
}
}Or for SSE:
{
"servers": {
"pipen-mcp": {
"type": "sse",
"url": "http://127.0.0.1:8520/sse"
}
}
}Any package can register processes/pipelines with pipen-mcp by declaring a pipen_cli_run entry point:
# pyproject.toml
[project.entry-points."pipen_cli_run"]
myns = "mypackage.ns.myns"The referenced module should contain Proc subclasses (with an input attribute) or ProcGroup subclasses. Use pipen-annotate to document arguments — annotated fields are exposed in get_process output and used to build the tool schema.
# mypackage/ns/myns.py
"""My namespace — tools for processing text files."""
from pipen import Proc
from pipen_annotate import annotate
@annotate
class MyProc(Proc):
"""Concatenate rows from multiple files.
Input:
infiles (list): Input files to concatenate
Envs:
sep (str): Column separator. Default: ","
"""
input = "infiles:files"
output = "outfile:file:{{in.infiles[0] | stem}}_concat.tsv"
script = "..."MIT
This server cannot be deployed
Give any MCP-compatible AI assistant a builder for live, hosted web tools and workflows.
Find, vet, and run MCP tools through a secure audited gateway with prompt-injection risk scoring
Your org's AI agents, tasks, runs, search, and brain files as MCP tools and resources.
Zero-setup MCP gateway securely connecting AI to your tools with authentication and workflows
