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@cafferychen777/ChatSpatial

MCP server for spatial transcriptomics analysis

ChatSpatial gives MCP clients a structured way to analyze spatial transcriptomics data through curated tools and validated parameters. Instead of generating arbitrary scripts, the agent selects from analysis methods for loading data, visualization, spatial domains, deconvolution, cell communication, and related workflows.

44 stars13 forksPythonUpdated 1mo ago
Who it's for

Builders who use Claude Code, Codex, or other MCP clients for spatial transcriptomics analysis.

What it delivers

You can ask an agent to run spatial analysis workflows with more consistent, reproducible tool calls instead of ad-hoc code.

What it does

Schema-validated MCP tools

Exposes 20 MCP tools with validated parameters so the agent stays within known analysis paths.

Spatial transcriptomics workflows

Covers loading, preprocessing, visualization, spatial domains, deconvolution, communication, annotation, differential expression, trajectory, velocity, and spatial statistics.

Multi-client support

Works with any MCP-compatible client, including Claude Code, Claude Desktop, and Codex.

Docker runtime

Provides a container image for local or reproducible deployment.

Optional full method families

Supports an expanded `chatspatial[full]` install for more method backends when needed.

How to get it

  1. 1Codex
    codex mcp add chatspatial -- uvx --from chatspatial chatspatial server
  2. 2Claude Code
    claude mcp add --scope user chatspatial -- \
      uvx --from chatspatial chatspatial server
  3. 3The command above installs the standard runtime. To make all 15 composable Python method…
    uvx --from 'chatspatial[full]' chatspatial server
  4. 4Docker quick start
    docker pull ghcr.io/cafferychen777/chatspatial:v1.4.0
  5. 5Minimal example prompt
    Load /absolute/path/to/spatial_data.h5ad and show me the tissue structure

README

ChatSpatial

MCP server for spatial transcriptomics analysis via natural language

Paper MLGenX @ ICLR 2026 ENAR 2026 IBC 2026 CI PyPI Python 3.11-3.14 License: MIT Docs Docker

ChatSpatial Overview

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.


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 analysisQuick Start
  2. Choose optional method families or a persistent environmentInstallation Guide
  3. Configure another MCP clientConfiguration Guide
  4. Inspect or reproduce the manuscript resultsReproducibility 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.


Capabilities

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

CategoryExample methods
Data Loading & PreprocessingScanpy I/O, QC, Normalization, HVG, PCA, Neighbors
VisualizationSpatial plots, Embedding plots, Gene expression overlays
Spatial Domain IdentificationSpaGCN, STAGATE, GraphST, BANKSY, AESTETIK, Leiden, Louvain
DeconvolutionFlashDeconv, Cell2location, RCTD (spacexr or rctd-py), DestVI, Stereoscope, SPOTlight, Tangram, CARD
Cell-Cell CommunicationLIANA+, CellPhoneDB, CellChat (cellchat_r), FastCCC
Cell Type AnnotationTangram, scANVI, CellAssign, mLLMCelltype, scType, SingleR
Differential ExpressionWilcoxon, t-test, Logistic Regression, pyDESeq2
Trajectory InferenceCellRank, Palantir, DPT
RNA VelocityscVelo, VeloVI
Spatial StatisticsMoran'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 AnalysisGSEA, ORA, Enrichr, ssGSEA, Spatial EnrichMap
Spatially Variable GenesSpatialDE, SPARK-X, FlashS
Multi-sample IntegrationHarmony, BBKNN, Scanorama, scVI
CNV AnalysisInferCNVPy, Numbat
Spatial RegistrationPASTE, STalign

Documentation

GuideUse this when...
InstallationYou need optional methods or a persistent Python environment
DockerYou want a reproducible container runtime or local dependency resolution fails
ConfigurationYou need exact MCP client syntax or the runtime path model
Quick StartChatSpatial is installed and you want the first successful analysis
ConceptsYou need to choose an analysis strategy from a biological question
ExamplesYou want copy-pasteable natural-language workflow prompts
Methods ReferenceYou need canonical tool names, method names, parameters, and defaults
TroubleshootingSetup, data loading, or analysis behavior is not working
Full DocsYou want the complete documentation site

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.


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.


Contributing

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

MIT License · GitHub · Issues

Files in the repo

Repository payload28 top-level entries
  • .agents
  • .github
  • assets
  • chatspatial
  • constraints
  • docs
  • reproducibility
  • scripts
  • tests
  • .dockerignore
  • .gitignore
  • .pre-commit-config.yaml
  • .readthedocs.yaml
  • CHANGELOG.md
  • CITATION.cff
  • CODE_OF_CONDUCT.md
  • CONTRIBUTING.md
  • DOCKER.md
  • Dockerfile
  • install_r_dependencies.R
  • INSTALLATION.md
  • LICENSE
  • Makefile
  • MANIFEST.in
  • pyproject.toml
  • README.md
  • SECURITY.md
  • server.json

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