ChatSpatial MCP Server

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ChatSpatial

MCP server for spatial transcriptomics analysis via natural language

</div> <p align="center"> <img src="assets/images/overview.jpg" alt="ChatSpatial Overview" width="900"> </p>

ChatSpatial replaces ad-hoc LLM code generation with schema-enforced orchestration. Instead of generating arbitrary scripts, the LLM selects tools and parameters from a curated registry, making spatial transcriptomics workflows more reproducible across sessions and clients.

ChatSpatial exposes 20 schema-validated MCP tools that orchestrate 66 spatial transcriptomics methods across 15 analytical categories. The tools are the stable natural-language interface; the methods are the analysis backends selected through tool parameters.

The server implements MCP 2026-07-28 through the official Python SDK v2 and continues to serve 2025-11-25 clients through SDK-managed protocol negotiation. STDIO remains the secure local default; Streamable HTTP is available for explicitly configured HTTP deployments.

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Start Here

Install uv once, then register ChatSpatial without creating or managing a Python environment:

Codex:

codex mcp add chatspatial -- uvx --from chatspatial chatspatial server

Claude Code:

claude mcp add --scope user chatspatial -- \
  uvx --from chatspatial chatspatial server

uvx creates an isolated environment on first launch and reuses its cache on later launches. Restart the MCP client after adding the server.

The command above installs the standard runtime. To make all 15 composable Python method families available in the same isolated MCP environment, use:

uvx --from 'chatspatial[full]' chatspatial server

full includes CellRank, FastCCC, the maintained spatial-domain and registration backends, annotation, enrichment, and the other portable Python families. R bridges, AESTETIK, and rctd-py remain separate because they have system, platform, or large-runtime requirements. See the installation guide before enabling those families.

Then:

1. Run your first analysis — Quick Start
2. Choose optional method families or a persistent environment — Installation Guide
3. Configure another MCP client — Configuration Guide
4. Inspect or reproduce the manuscript results — Reproducibility workspace

Docker quick start:

docker pull ghcr.io/cafferychen777/chatspatial:v1.4.0

Minimal example prompt:

Load /absolute/path/to/spatial_data.h5ad and show me the tissue structure

If you use Docker, mount host data to /data and prompt with the container path, for example /data/spatial_data.h5ad.

ChatSpatial works with any MCP-compatible client — Claude Code, Claude Desktop, Codex, OpenCode, and other MCP-capable tools.

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Capabilities

Current coverage includes 66 methods across 15 analytical categories, exposed through 20 MCP tools. Supports 10x Visium, Xenium, Slide-seq v2, MERFISH, seqFISH.

Category	Example methods
Data Loading & Preprocessing	Scanpy I/O, QC, Normalization, HVG, PCA, Neighbors
Visualization	Spatial plots, Embedding plots, Gene expression overlays
Spatial Domain Identification	SpaGCN, STAGATE, GraphST, BANKSY, AESTETIK, Leiden, Louvain
Deconvolution	FlashDeconv, Cell2location, RCTD (spacexr or rctd-py), DestVI, Stereoscope, SPOTlight, Tangram, CARD
Cell-Cell Communication	LIANA+, CellPhoneDB, CellChat (cellchat_r), FastCCC
Cell Type Annotation	Tangram, scANVI, CellAssign, mLLMCelltype, scType, SingleR
Differential Expression	Wilcoxon, t-test, Logistic Regression, pyDESeq2
Trajectory Inference	CellRank, Palantir, DPT
RNA Velocity	scVelo, VeloVI
Spatial Statistics	Moran's I, Local Moran, Geary's C, Getis-Ord Gi*, Ripley's K, Co-occurrence, Neighborhood Enrichment, Centrality Scores, Local Join Count, Network Properties
Enrichment Analysis	GSEA, ORA, Enrichr, ssGSEA, Spatial EnrichMap
Spatially Variable Genes	SpatialDE, SPARK-X, FlashS
Multi-sample Integration	Harmony, BBKNN, Scanorama, scVI
CNV Analysis	InferCNVPy, Numbat
Spatial Registration	PASTE, STalign

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Documentation

Guide	Use this when...
Installation	You need optional methods or a persistent Python environment
Docker	You want a reproducible container runtime or local dependency resolution fails
Configuration	You need exact MCP client syntax or the runtime path model
Quick Start	ChatSpatial is installed and you want the first successful analysis
Concepts	You need to choose an analysis strategy from a biological question
Examples	You want copy-pasteable natural-language workflow prompts
Methods Reference	You need canonical tool names, method names, parameters, and defaults
Troubleshooting	Setup, data loading, or analysis behavior is not working
Full Docs	You want the complete documentation site

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Reproducibility

The manuscript experiment scripts, small aggregate result tables, and supplementary tables are versioned in reproducibility/. Large datasets, raw provider checkpoints, generated analysis directories, and manuscript source files are intentionally kept outside Git. The reproducibility workspace documents both the manuscript-era package baseline and the current-checkout development workflow so historical evidence is not silently regenerated with a different ChatSpatial release.

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Citation

If you use ChatSpatial in your research, please cite:

@article{Yang2026.02.26.708361,
  author = {Yang, Chen and Zhang, Xianyang and Chen, Jun},
  title = {ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics},
  elocation-id = {2026.02.26.708361},
  year = {2026},
  doi = {10.64898/2026.02.26.708361},
  publisher = {Cold Spring Harbor Laboratory},
  URL = {https://www.biorxiv.org/content/early/2026/03/01/2026.02.26.708361},
  journal = {bioRxiv}
}

ChatSpatial orchestrates many excellent third-party methods. Please also cite the original tools your analysis used.

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Contributing

Documentation improvements, bug reports, and new analysis methods are all welcome. See CONTRIBUTING.md.

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MIT License · GitHub · Issues

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