Infercnv-MCP
Provides a natural language interface for inferring Copy Number Variations (CNVs) from scRNA-Seq data using the infercnvpy framework. It enables users to perform data preprocessing, CNV inference, and visualization through chromosome heatmaps, UMAP, and t-SNE plots.
README
Infercnv-MCP
Natural language interface for Copy Number Variation (CNV) inference from scRNA-Seq data with infercnvpy through MCP.
đĒŠ What can it do?
- IO module for reading and writing scRNA-Seq data, load gene position
- Preprocessing module for neighbors computation and data preparation
- Tool module for CNV inference, cnv score
- Plotting module for chromosome heatmaps, UMAP, and t-SNE visualizations
â Who is this for?
- Researchers who want to infer CNVs from scRNA-Seq data using natural language
- Agent developers who want to integrate CNV analysis into their applications
đ Where to use it?
You can use infercnv-mcp in most AI clients, plugins, or agent frameworks that support the MCP:
- AI clients, like Cherry Studio
- Plugins, like Cline
- Agent frameworks, like Agno
đ Documentation
scmcphub's complete documentation is available at https://docs.scmcphub.org
đī¸ Quickstart
Install
Install from PyPI
pip install infercnv-mcp
you can test it by running
infercnv-mcp run
run infercnv-mcp locally
Refer to the following configuration in your MCP client:
check path
$ which infercnv
/home/test/bin/infercnv-mcp
"mcpServers": {
"infercnv-mcp": {
"command": "/home/test/bin/infercnv-mcp",
"args": [
"run"
]
}
}
Run infercnv-server remotely
Refer to the following configuration in your MCP client:
Run it in your server
infercnv-mcp run --transport shttp --port 8000
Then configure your MCP client, like this:
http://localhost:8000/mcp
đ¤ Contributing
If you have any questions, welcome to submit an issue, or contact me(hsh-me@outlook.com). Contributions to the code are also welcome!
Citing
If you use infercnv-mcp in your research, please consider citing following work:
https://github.com/icbi-lab/infercnvpy
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.