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cxg-census-mcp

by MaxMLangversion 0.1.2i★ –iRuns on your computer
Registry nameio.github.MaxMLang/cxg-census-mcp

Install

Overview

Description from the maker (original English)

MCP server for the CZ CELLxGENE Census. Single-cell, ontology-aware. Community, unaffiliated.

PopNx MCP’s three pillars

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

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?

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": {
    "cxg-census": {
      "command": "uvx",
      "args": [
        "cxg-census-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add cxg-census -- uvx cxg-census-mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "cxg-census": {
      "command": "uvx",
      "args": [
        "cxg-census-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": {
    "cxg-census": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "cxg-census-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 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account MaxMLang, 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 32727 servers in this category
  • Published to the registry more recently than 7% of the other 32727 servers in this category
  • Basic checks equal to or better than 100% of the other 32727 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 cxg-census-mcp 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 (0.1.2) reached the registry 7 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, Foliosio

Alternatives

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

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Foliosio

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Read your support inbox, tag conversations and save draft replies from your AI agent. Never sends.

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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.

cxg-census-mcp

An MCP server that lets LLM agents query the CZ CELLxGENE Discover Census single-cell atlas without lying about it — ontology-aware filters, cost caps, full provenance + attribution on every response. Drop it into Cursor / Claude Desktop / Claude Code and ask questions like *"Visualize the cell-type composition of the human lung"* in plain English.

Independent / unaffiliated. Not affiliated

with, endorsed by, or sponsored by the Chan Zuckerberg Initiative (CZI),

EMBL-EBI, the U.S. Census Bureau, or anyone else. "CELLxGENE" is a CZI

mark; references here are descriptive (nominative) use only.

No warranty. MIT-licensed source, "as is". Research/exploration tool —

not a clinical or diagnostic instrument. Always verify results before

publication. See LICENSE

for the full trademark and content attribution notice, and

SECURITY.md

for the threat model and known-issues policy.

Alpha (v0.1.2). CHANGELOG.md

Demos

Healthy vs COVID-19 lung, side-by-side. Two parallel queries, the disease_multi_value_v7 schema-drift rewrite kicks in for the COVID cohort, attribution from both contributing dataset sets surfaces in the same chat turn.

https://github.com/user-attachments/assets/c836f225-5075-4643-87aa-70d311bc5fd2

Cell-type composition of human lung in one query. Free-text "lung" resolved to UBERON:0002048, routed through tissue_general, every CURIE labeled, all in a single Tier-0 call.

https://github.com/user-attachments/assets/b0e10ca7-e46b-4e5f-ae63-11949d328c4d

(Videos render on GitHub. On PyPI they appear as bare URLs — head to the GitHub README to watch.)

More prompts in docs/example-questions.md.

Architecture at a glance

                 ┌──────────────────────────────────────────────┐

MCP client │ tools/ thin MCP wrappers, no logic │ (Claude, ─► │ │ │ Cursor, │ ▼ │ Code, …) │ planner/ FilterSpec → QueryPlan, │ │ │ cost estimate, tier routing │ │ ▼ │ │ ontology/ OLS4 + hint overlay, │ │ │ CL/UBERON/MONDO expansion │ │ ▼ │ │ execution/ Tier 0 facet counts │ │ │ Tier 1 chunked obs scan │ │ │ Tier 2 expression aggregate │ │ │ Tier 9 refuse → snippet │ │ ▼ │ │ clients/ OLS4 (HTTPS) + Census/SOMA │ │ │ │ caches/ OLS, facet, plan, filter LRU │ │ models/ Response envelope w/ │ │ attribution + provenance │ └──────────────────────────────────────────────┘ │ ▼ ┌────────────────────────┐ │ EBI OLS4 (ontology) │ │ CZ CELLxGENE Census │ │ (CC BY 4.0 data) │ └────────────────────────┘

Full architecture notes: docs/architecture.md. Tool reference: docs/tool-reference.md. Example questions: docs/example-questions.md.

Install

From PyPI (recommended):

uv tool install "cxg-census-mcp[census]"

cxg-census-mcp # speaks MCP over stdio

Or with pip:

pip install "cxg-census-mcp[census]"

Without the [census] extra you get mock mode (deterministic fixtures) — handy for offline demos and verifying your MCP client config without pulling tiledbsoma's ~1 GB of native deps.

From source (for development):

git clone https://github.com/MaxMLang/cxg-census-mcp

cd cxg-census-mcp uv sync --extra dev --extra census uv run cxg-census-mcp

MCP client config

Cursor (~/.cursor/mcp.json) and Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS) both expect the same shape. Cleanest is uvx once installed from PyPI:

{

"mcpServers": { "cxg-census": { "command": "/absolute/path/to/uvx", "args": ["--from", "cxg-census-mcp[census]", "cxg-census-mcp"] } } }

Use the absolute path to uvx (which uvx from your shell). MCP

clients spawn the server in a non-interactive subprocess that doesn't

source your shell rc, so a bare "uvx" will fail with

No such file or directory.

If you cloned from source instead, point at the checkout:

{

"mcpServers": { "cxg-census": { "command": "/absolute/path/to/uv", "args": ["--directory", "/path/to/cxg-census-mcp", "run", "cxg-census-mcp"] } } }

Claude Code:

claude mcp add cxg-census -- /absolute/path/to/uvx --from "cxg-census-mcp[census]" cxg-census-mcp

Quit + relaunch your client (⌘Q on macOS — closing the window isn't enough) and the server should show up in the MCP panel with 13 tools.

Tools (13 total)

Workflow: census_summary, get_census_versions, count_cells, list_datasets, gene_coverage, aggregate_expression, preview_obs, export_snippet, get_server_limits.

Inspection: resolve_term, expand_term, term_definition, list_available_values.

Plus MCP resources (markdown docs at cxg-census-mcp://docs/{slug}), prompts (census_workflow, disambiguation), and cooperative progress / cancellation notifications. Details in docs/tool-reference.md.

Configuration

All env vars use the CXG_CENSUS_MCP_ prefix. Most useful:

VariableDefaultPurpose
CXG_CENSUS_MCP_CENSUS_VERSIONstableCensus release to pin
CXG_CENSUS_MCP_CACHE_DIRplatformdirs defaultDisk cache root
CXG_CENSUS_MCP_MOCK_MODE0If 1, never opens a real Census handle
CXG_CENSUS_MCP_LOG_LEVELWARNINGstdlib log level

Full list and validation: src/cxg_census_mcp/config.py.

Development & operations

Quick loop:

make install-all                 # uv sync --extra dev --extra census

make lint typecheck test # ruff + mypy + pytest (mock mode) make cov # tests + coverage HTML in ./htmlcov make audit # pip-audit on locked production deps

Operational tasks (cache pre-warm, schema diff, container build, metrics dump, plan-cache vacuum, weekly hint/facet refresh) live in the Makefile and are documented in docs/operational-playbook.md.

Documentation index

TopicWhere
System architecturedocs/architecture.md
Tool referencedocs/tool-reference.md
Example agent questionsdocs/example-questions.md
Ontology resolutiondocs/ontology-resolution.md
Schema-drift handlingdocs/schema-drift-format.md
Census version pinningdocs/version-pinning.md
Progress / cancellationdocs/progress-and-cancellation.md
Error modeldocs/error-model.md
Known limitationsdocs/limitations.md
Ops runbookdocs/operational-playbook.md
ChangelogCHANGELOG.md

License & attribution

Source code: MIT. The MIT license covers only the code in this repository, not the upstream data, ontologies, or third-party trademarks.

  • Data. Tool responses are derived (filtered/aggregated) from the CZ CELLxGENE Discover Census, distributed by the Chan Zuckerberg Initiative under CC BY 4.0. Every response carries an attribution field; downstream users must preserve attribution and indicate that changes were made.
  • Ontologies are fetched via EBI Ontology Lookup Service (OLS4) from CL, UBERON, MONDO, EFO, HANCESTRO, and others; each carries its own license.
  • Trademarks ("CELLxGENE", "Cursor", "Claude", "Anthropic", "Model Context Protocol", …) belong to their respective owners. Use here is descriptive only and does not imply affiliation.

This project is a *client* of the CZ CELLxGENE Discover Census; it does not host, mirror, or redistribute Census data.

Full notice in LICENSE.

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