OpenHCS

Name: OpenHCS
Author: OpenHCSDev

by OpenHCSDevversion 0.8.6i★ –iRuns on your computer

Registry nameio.github.OpenHCSDev/openhcs

Install

Overview

Description from the maker (original English)

Inspect, author, validate, and run high-content microscopy workflows.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "openhcs": {
      "command": "uvx",
      "args": [
        "openhcs",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add openhcs -- uvx openhcs mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "openhcs": {
      "command": "uvx",
      "args": [
        "openhcs",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "openhcs": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "openhcs",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account OpenHCSDev, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32417 servers in this category
Published to the registry more recently than 45% of the other 32417 servers in this category
Basic checks equal to or better than 100% of the other 32417 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is OpenHCS safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.8.6) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, FOGA node

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

OpenHCS

Turn high-content microscopy images into reproducible measurements\ One reviewable workflow across the GUI, Python, CellProfiler, and local agents

OpenHCS is designed for imaging scientists and research software teams running high-content studies where many wells, sites, channels, Z planes, or time points must be analysed consistently. Source selection, processing steps, and result definitions stay together in one validated pipeline instead of being split across interface-only state, scripts, and automation.

It is a good fit when a workflow must remain reviewable across visual editing, code, and automation. The same pipeline can be edited in the desktop GUI or as Python, imported from supported CellProfiler .cppipe files, and built or reviewed through the local MCP surface.

Install

Windows installer · macOS installer · Installation options

The graphical installers set up an isolated CPU-safe desktop environment with the OpenHCS GUI, CellProfiler compatibility, local MCP server, Napari, Fiji/ImageJ, and Bio-Formats. GPU libraries remain optional.

See OpenHCS in use

Browse the UI and viewer gallery · Watch an agent build, debug, run, and inspect a workflow

OpenHCS processes large microscopy datasets with a compile-then-execute architecture. Pipelines are validated across the selected execution axes *before* processing starts, preventing late failures after expensive work. Design pipelines in the GUI, export to Python, edit as code, and re-import — switching between visual and programmatic workflows. The local MCP exposes that same workflow model to supported agents, so agent-authored pipelines remain visible, editable, and reviewable in the GUI and generated Python.

graph LR

subgraph Sources IX[ImageXpress] OP[Opera Phenix] BF[Bio-Formats] OM[OMERO] end

subgraph OpenHCS Platform PD["Pipeline Designer (GUI ⇄ Code ⇄ Agent)"] CO["Typed Compiler (resolve + validate)"] EX["Bounded Worker Executor (well scheduling · multi-GPU)"] FN["Registry-Discovered Functions scikit-image · CuPy · pyclesperanto PyTorch · JAX · TF · CuCIM · custom"] PS["PolyStore (Memory ↔ Disk ↔ Zarr ↔ Stream)"] end

subgraph Viewers NA[Napari] FJ[Fiji/ImageJ] end

IX --> PD OP --> PD BF --> PD OM --> PD PD --> CO --> EX EX --> FN --> PS PS --> NA PS --> FJ

⚡ Key Capabilities

🛡️ Compile-Time Validation

Configuration is resolved once into step snapshots and a compilation session. Typed plans then validate sources, artifacts, materialization, memory contracts, and worker requirements before execution begins. Errors surface immediately, not after hours of processing.

🔄 Bidirectional GUI ↔ Code

Design pipelines visually, export as executable Python, edit in your IDE, re-import to the GUI. Code generation works at any scope level — function patterns, individual steps, pipeline configs, full orchestrator scripts — any window holding objects can generate and re-import code.

🧠 Agent-Assisted Workflows

Give a supported MCP client a microscopy folder or plate and an analysis goal. It can inspect the connected execution server's functions, build and validate a typed pipeline, run it, inspect results in OpenHCS or a viewer, and revise the generated Python.

⚡ Multiprocessing & GPU Acceleration

Bounded worker lanes use ProcessPoolExecutor by default, with deterministic well assignment and sequential processing inside each lane. Compiled callable contracts select framework-local GPU devices independently; single-worker and debugging configurations can use inline or threaded execution.

🔌 Any Python Function

Register any Python function by decorating it with @numpy, @cupy, @pyclesperanto, @torch, or another memory-type decorator. Custom functions receive contract validation, UI integration, multiprocessing-safe import identity, and the same server-owned catalog projection as built-in functions. Persisted functions live in the platform-specific OpenHCS user-data directory.

📊 Results Materialization

Callable and module artifact contracts declare semantic outputs independently of Python argument names. The artifact graph and materialization plans route images, measurements, object labels, relationships, tables, and files to their configured stores and exporters.

🔬 Process-Isolated Napari & Fiji

Stream images to Napari and Fiji/ImageJ in real time during pipeline execution. OpenHCS StreamingConfig declarations and viewer adapters own identity, display, and persistence policy. PolyStore builds generic storage and streaming payloads; ZMQRuntime supplies process-isolated transport, readiness, acknowledgments, and lifecycle.

🪟 Live Cross-Window Updates

Edit a value in GlobalPipelineConfig — watch it propagate in real-time to PipelineConfig and StepConfig windows. Dual-axis resolution (context hierarchy × class MRO) with scope isolation per orchestrator.

🧬 CellProfiler Pipeline Import

Open .cppipe files in the desktop application or lower them from Python into ordinary PipelineConfig and FunctionStep declarations. Named images, objects, measurements, relationships, and exports use the same typed compiler and runtime as native OpenHCS pipelines. The source-backed Official30 suite continuously exercises 30 pipelines from CellProfiler examples, tutorials, and benchmark supplements under explicit equivalence policies.

🤖 MCP Agent Automation

Use the local stdio MCP server with ChatGPT desktop, Codex, Claude Desktop, and other supported clients, or deploy the separately secured HTTP surface. The graphical installers register detected local clients automatically. Capability profiles, schemas, knowledge, UI attachment, authoring, execution, runtime inspection, viewer review, and governed custom-function registration are projected from typed authorities rather than duplicated tool lists.

🧩 The OpenHCS Ecosystem

OpenHCS is built on 8 purpose-extracted, separately published libraries — each solving a general problem and all composed into one platform:

graph TD

OH["OpenHCS Platform (domain wiring + pipelines)"]

OH --> OS["ObjectState (config)"] OH --> AB["ArrayBridge (arrays)"] OH --> PS["PolyStore (I/O + streaming)"] OH --> ZR["ZMQRuntime (exec)"] OH --> QR["PyQT-reactive (Qt forms)"] OH --> FB["formbridge (form model + UI bridge)"] QR --> FB

OS --> PI["python-introspect (signatures)"] OH --> MR["metaclass-registry (plugins)"] OH --> PC["pycodify (serialization)"]

Library	Role in OpenHCS	What It Does
ObjectState	Configuration framework	Lazy dataclasses with dual-axis inheritance (context hierarchy × class MRO) and `contextvars`-based resolution
ArrayBridge	Memory type conversion	Unified API across NumPy, CuPy, PyTorch, JAX, TensorFlow, pyclesperanto with DLPack zero-copy transfers
PolyStore	Unified I/O & stream payloads	Generic storage and streaming payload primitives, backend lifecycle, virtual workspaces, atomic writes, format detection, and ROI extraction
ZMQRuntime	Process & transport runtime	Generic request, status, progress, cancellation, process-lifecycle, and viewer-control transport protocols
PyQT-reactive	UI form generation	React-style reactive forms from dataclasses with cross-window sync and flash animations
formbridge	Toolkit-neutral forms and UI bridge	The form model, help and code documents, and the running-UI bridge contracts, client and server; imports no GUI toolkit, so headless MCP uses it without Qt
pycodify	Code ↔ object conversion	Python source as serialization format — type-preserving, diffable, editable, with collision handling
python-introspect	Signature analysis	Pure-Python function/dataclass introspection for automatic UI generation and contract analysis
metaclass-registry	Plugin discovery	Zero-boilerplate registry system powering microscope handler and storage backend auto-discovery

🔬 Microscope & Function Support

Image Sources

Source	Support
ImageXpress	Native plate and metadata handling
Opera Phenix	Native plate and metadata handling
Bio-Formats	Arbitrary folders and supported microscopy containers
OMERO	Remote image and metadata access
OpenHCS format	Native generated and materialized plates

Source handlers are auto-detected and extensible through metaclass-registry.

Functions — Automatic Discovery

Library or route	Execution memory
scikit-image and OpenHCS native	NumPy / CPU
pyclesperanto	OpenCL GPU
CuPy and cuCIM	CUDA GPU
PyTorch, JAX, and TensorFlow functions	Declared CPU/GPU arrays
User custom functions	Declared by their memory-type decorator

The connected execution server owns the available catalog, so remote GPU and custom-function availability is reflected without a manually maintained list. ArrayBridge provides compatible memory conversion, including zero-copy paths where supported.

Processing domains: image preprocessing · segmentation · cell counting · stitching (MIST + Ashlar GPU) · neurite tracing · morphology · measurements

Dimensionality is function-defined rather than a global mode: true volumetric segmentation and measurement routes coexist with plane-local functions, whose labels are not silently stitched across Z. See the [dimensionality and measurement capabil

Only the beginning is shown; read the rest in the repo

OpenHCS

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

FOGA node

README from the maker

Install

See OpenHCS in use

⚡ Key Capabilities

🛡️ Compile-Time Validation

🔄 Bidirectional GUI ↔ Code

🧠 Agent-Assisted Workflows

⚡ Multiprocessing & GPU Acceleration

🔌 Any Python Function

📊 Results Materialization

🔬 Process-Isolated Napari & Fiji

🪟 Live Cross-Window Updates

🧬 CellProfiler Pipeline Import

🤖 MCP Agent Automation

🧩 The OpenHCS Ecosystem

🔬 Microscope & Function Support