Using LLM Engines with scvi-tools

Using LLM Engines with scvi-tools#

Large language models (LLMs) can significantly lower the barrier to using scvi-tools by helping researchers write code, choose models, tune parameters, and troubleshoot analyses through natural language. This page covers how to leverage popular AI platforms—Claude, ChatGPT, OpenClaw, Gemini, and BioMNI—to get the most out of scvi-tools.

scvi-tools MCP Server#

The scvi-tools MCP server (scvi-tools-mcp) is a Model Context Protocol server that gives any MCP-compatible LLM structured access to scvi-tools knowledge: model documentation, tutorials, API reference, workflow templates, and a curated FAQ—with no runtime model execution.

Installation#

pip install scvi-tools-mcp

Or use uvx (no install needed) directly in your MCP client config.

Connecting to your LLM client#

Claude Code (CLI)

claude mcp add scvi-tools-mcp -s user -- uvx scvi-tools-mcp

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "scvi-tools-mcp": {
      "command": "uvx",
      "args": ["scvi-tools-mcp"]
    }
  }
}

OpenAI Codex CLI — add to ~/.codex/config.toml:

[mcp_servers.scvi-tools-mcp]
command = "uvx"
args   = ["scvi-tools-mcp"]

Cursor / Windsurf / other MCP-compatible clients

{ "command": "uvx", "args": ["scvi-tools-mcp"] }

Available tools#

Tool	Description
`recommend_model`	Rank models by task and data type — start here
`get_model_overview`	Full model description, use cases, inputs/outputs
`get_model_parameters`	Key `__init__` and `train()` parameters with defaults
`get_setup_anndata_guide`	Exact `setup_anndata()` call + required obs/var fields
`validate_data_requirements`	Pass/fail checklist for your AnnData against a model
`list_tutorials`	Browse tutorials by category
`get_tutorial`	Paginated tutorial content (code + prose)
`search_tutorials`	Keyword search across all tutorials
`get_api_reference`	Signature + docstring for any public symbol
`search_api`	Search public symbols by keyword
`get_workflow_template`	Step-by-step code template for an analysis task
`get_downstream_guide`	Guide for DE, clustering, embedding, label transfer
`get_faq`	Curated FAQ from docs, GitHub issues, and Discourse
`search_knowledge`	Cross-search all knowledge (catch-all)

Once connected, you can ask questions like:

“What model should I use for CITE-seq data with strong batch effects?”

“Show me a full scVI workflow template with batch correction.”

“My KL divergence is exploding during training — how do I fix it?”

The server resolves these against baked-in knowledge (no network calls at tool-call time) and returns structured, scvi-tools-specific guidance.

Claude (Anthropic)#

Claude is a general-purpose AI assistant from Anthropic. For scvi-tools users, Claude offers two complementary integrations: the scvi-tools MCP Server (see above) for real-time knowledge lookup inside Claude Code and Claude Desktop, and a scvi-tools Skill Bundle with guided workflows for the full scvi-tools ecosystem.

scvi-tools Skill Bundle#

The skill bundle gives Claude deep knowledge of scvi-tools workflows and includes guidance for:

Batch integration: scVI and scArches
Cell type annotation: SCANVI and CellAssign
Spatial analysis: DestVI, Tangram, Cell2location, Stereoscope
Epigenetic data: PeakVI and scBasset
Multimodal integration: TotalVI (CITE-seq) and MultiVI (RNA+ATAC)
Perturbation studies: contrastiveVI

Each skill covers recommended workflows, parameter guidance, and troubleshooting tips.

Installation#

Claude Code users:

/plugin install scvi-tools@life-sciences

Claude.ai users: Organization admins can upload the skill ZIP via Admin Settings > Skills. Individual users can upload via Settings > Capabilities > Skills. Download instructions and the skill ZIP are provided in the Anthropic tutorial.

Once installed, you can ask questions like:

“I have 10x Chromium data from 3 donors with different sequencing depths. Which scvi-tools model should I use for integration, and what batch key should I set?”

See the full tutorial at Anthropic’s scvi-tools Skill Bundle guide.

ChatGPT (OpenAI)#

ChatGPT can assist with scvi-tools through two complementary routes: custom GPTs and MCP (Model Context Protocol) tool integrations.

Custom GPTs#

OpenAI’s GPT Store hosts community-built GPTs specialized in single-cell analysis. For example, the Scanpy – Your Single-Cell RNA-seq Data Analyst GPT is configured to assist with scanpy-based workflows, which pair naturally with scvi-tools preprocessing pipelines.

You can use such GPTs to:

Walk through an end-to-end scRNA-seq analysis
Get scvi-tools code snippets for common operations
Debug errors from scvi-tools model training

MCP (Model Context Protocol) Tool Use#

OpenAI supports tool use via the API, which enables agents to call external functions—including Python code execution. This makes it possible to build automated pipelines where ChatGPT generates and runs scvi-tools code on your data.

A simple agent prompt example:

“Load the AnnData file at data/pbmc.h5ad, run scVI with 2 batches defined by adata.obs['batch'], train for 400 epochs, and return the UMAP coordinates.”

With tool use enabled, ChatGPT can generate the code and invoke a Python execution environment to produce results.

OpenClaw#

OpenClaw (available via the LobeHub market) provides an installable skill focused on scvi-tools for use with Claude-based agents. It is optimized for researchers who need rigorous statistical frameworks and multi-batch integration.

Installation#

# Register your agent (one-time)
npx -y @lobehub/market-cli register --name "YourName" --source open-claw

# Install the scvi-tools skill
npx -y @lobehub/market-cli skills install k-dense-ai-claude-scientific-skills-scvi-tools

After installation, read the SKILL.md file in the extracted directory for usage instructions. The skill covers:

Probabilistic batch correction and dataset alignment
Multi-modal analysis (CITE-seq, spatial, multiome)
Uncertainty quantification in differential expression
Cell annotation with transfer learning

This skill is best suited for users who want a lightweight Claude-compatible skill without requiring the full Claude.ai platform.

Gemini (Google)#

Gemini is Google’s general-purpose LLM, accessible via Google AI Studio and the Gemini API. While there is no dedicated scvi-tools skill for Gemini, it is effective for assisted code generation, debugging, and conceptual guidance when working with scvi-tools.

General Use#

Gemini can help with scvi-tools through natural language prompting for:

Generating scvi-tools setup and training code
Explaining model outputs and hyperparameters
Suggesting appropriate models for your data type

Example prompt in AI Studio:

“Write Python code to train an scVI model on an AnnData object with a batch column called sample_id, then extract the latent embedding and run a UMAP.”

You can paste scvi-tools error messages, documentation excerpts, or code snippets directly into the chat to get targeted assistance.

BioMNI (Stanford)#

BioMNI is a general-purpose biomedical AI agent from Stanford, designed to autonomously execute research tasks across diverse biomedical subfields. It has a native integration with the scverse ecosystem—including scvi-tools—announced in 2025.

Integration with scverse and scvi-tools#

BioMNI understands biological context and can orchestrate multi-step pipelines across scverse packages (Scanpy, scvi-tools, Squidpy, Pertpy) from plain-language instructions. Crucially, all agent-generated code is packaged as reproducible Jupyter notebooks.

Example prompts:

“Run QC and normalization, integrate my three batches using scVI, cluster the cells, and annotate cell types using SCANVI.”

“Cluster cells and identify marker genes for each cluster.”

BioMNI handles parameter selection, dependency management, and returns documented, reproducible results—no manual coding required.

Access#

Web platform: biomni.stanford.edu — interactive, no setup required
Open-source: github.com/snap-stanford/Biomni — self-hosted deployment

BioMNI is particularly well-suited for biologists who want to run complete single-cell and spatial workflows without writing code, while still producing reproducible, shareable analyses.

Summary#

Platform	Best For	scvi-tools Integration
Any MCP client	Real-time API/tutorial/FAQ lookup inside the LLM	`scvi-tools-mcp` — works with Claude Code, Claude Desktop, Cursor, Codex, and more
Claude	Guided workflows, parameter tuning, troubleshooting	MCP server + dedicated skill bundle with full model coverage
ChatGPT / Codex	Code generation, custom GPTs, agentic pipelines	Custom GPTs + MCP tool use via `scvi-tools-mcp`
OpenClaw	Lightweight Claude-based skill, CLI install	Installable scvi-tools skill via LobeHub
Gemini	General code assistance, AI Studio prompting	General LLM assistance; MCP support via Gemini CLI
BioMNI	End-to-end automated scverse pipelines	Native scverse/scvi-tools integration

Each platform offers a different trade-off between ease of use, customization, and depth of scvi-tools knowledge. The scvi-tools-mcp server is the broadest integration: install once and any MCP-compatible client gains structured scvi-tools knowledge. For users who want fully guided workflows, Claude’s skill bundle or BioMNI provide the deepest experience. For custom pipelines or agentic workflows, scvi-tools-mcp combined with a code-execution tool (ChatGPT function calling, Claude tool use) or BioMNI’s open-source deployment offer the most flexibility.

Note

LLM-generated code should always be reviewed before running on important data. Check that model parameters, batch keys, and data shapes match your specific dataset.

Using LLM Engines with scvi-tools

Contents

Using LLM Engines with scvi-tools#

scvi-tools MCP Server#

Installation#

Connecting to your LLM client#

Available tools#

Claude (Anthropic)#

scvi-tools Skill Bundle#

Installation#

ChatGPT (OpenAI)#

Custom GPTs#

MCP (Model Context Protocol) Tool Use#

OpenClaw#

Installation#

Gemini (Google)#

General Use#

BioMNI (Stanford)#

Integration with scverse and scvi-tools#

Access#

Summary#