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Health: ActiveRecent health check succeeded.Last checked 9/7/2026, 10:46:46 PM

PaperBanana CN

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe havenโ€™t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time โ€” check back soon.
View Repository79 GitHub StarsTotal stargazers on GitHub for the source repository (79 stars).Visit Website

Generate and evaluate academic diagrams and plots with independent model connections.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag โ€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON โ–พ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "paperbanana-cn": {
      "command": "uvx",
      "args": [
        "paperbanana-cn"
      ]
    }
  }
}

๐Ÿ’ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives๐Ÿ’ป More in Developer Tools

Documentation Overview

English ยท ็ฎ€ไฝ“ไธญๆ–‡

PaperBanana-CN scientific figure workbench with a generated multimodal fault-diagnosis figure

PaperBanana-CN generates methodology diagrams from research descriptions and statistical plots from CSV or JSON data. It uses the PaperBanana scientific workflow and adds separate VLM and image-service connections, a Chinese Studio interface, and explicit aspect-ratio and resolution controls.

Launch Studio Install from PyPI

CI status Package version 2.0.1 MIT License

One run from input to figure

A PaperBanana-CN Studio run progressing from configured inputs to a completed scientific figure

The recording follows a methodology-diagram run from submitted inputs to the completed result.

Before you start

You need Python 3.10-3.12, uv, and a desktop browser.

TaskRequired model connections
Methodology diagramVLM and image generation
Statistical plotVLM only
Multi-panel composition and run browsingNone

Each connection specifies its own protocol, Base URL, API key, model name, and timeout. The VLM and image roles may use the same service or two different services.

Quick start

1. Launch Studio

bash
uvx paperbanana-cn studio

Open http://127.0.0.1:7860. uvx runs the package in an isolated environment and does not modify Debian or Ubuntu's system Python.

2. Add the model connections

Open Settings โ†’ VLM connection, fill in the service fields, and select Save and use. Repeat under Image connection before generating a methodology diagram.

Editing a saved connection does not activate it. An empty API-key field keeps the stored key. Studio does not fill stored keys back into the browser.

Connection manager screenshot and protocol notes

PaperBanana-CN connection manager with separate fields for a VLM service

The connection guide lists the supported protocols, credential storage rules, connection tests, and legacy mode.

3. Generate a figure

Open Methodology diagram, provide the method content and figure caption, then choose an aspect ratio, resolution, and output format.

The same task from the CLI:

bash
paperbanana-cn generate \
  --input method.txt \
  --caption "Overview of the proposed architecture" \
  --aspect-ratio 16:9 \
  --resolution 2K \
  --format png

What PaperBanana-CN adds

Separate VLM and image connections

The two model roles have independent protocol, Base URL, API key, model, and timeout settings. Studio, CLI, and MCP resolve the same saved connections. Saved profiles contain credential references; API keys remain outside the repository and run metadata.

Official APIs, OpenAI-compatible services, and Gemini-compatible services are supported. Provider specifics stay in the adapters rather than the scientific workflow.

Chinese and English Studio

The Studio interface, help text, validation, progress messages, and errors are available in Chinese and English. Changing the interface language does not rewrite prompts, paper text, or labels inside the generated figure.

Aspect ratios and resolution

Supported aspect ratios:

1:1 ยท 4:3 ยท 3:2 ยท 5:4 ยท 16:9 ยท 21:9 ยท 4:5 ยท 3:4 ยท 2:3 ยท 9:16

Resolution tiers:

1K ยท 2K ยท 4K

Studio shows the request size or provider-native tier before generation. If an adapter cannot produce the selected combination, validation stops the request and reports the unsupported option.

Studio workflows

AreaWorkflowModel connections
CreateMethodology diagramVLM and image
CreateStatistical plotVLM
ImproveContinue a saved runDepends on the saved run
ImproveQuality evaluationVLM
AutomateFull-paper orchestrationVLM and image
AutomateBatch generationDepends on the task type
AutomateParameter sweepVLM and image
ToolsMulti-panel compositionNone
ToolsRun browserNone

PaperBanana-CN methodology-diagram workspace with task inputs and a completed result

Methodology-diagram workspace

PaperBanana-CN statistical-plot workspace with CSV input and a completed line chart

Statistical-plot workspace using synthetic demonstration data

CLI, MCP, Docker, and Colab

Entry pointCommand or link
Studiopaperbanana-cn studio
CLIpaperbanana-cn generate --help
MCP serverpaperbanana-cn mcp
GitHub ActionAction reference
Dockerghcr.io/mituan-ai/paperbanana-cn:2.0.1
ColabQuickstart notebook
MCP client configuration
config.json
{
  "mcpServers": {
    "paperbanana-cn": {
      "command": "uvx",
      "args": ["paperbanana-cn", "mcp"]
    }
  }
}

The server provides 11 tools and reads the same active connections as Studio and CLI. See the MCP guide for the tool list and arguments.

Docker
Terminal
docker run --rm -p 7860:7860 \
  -v paperbanana-cn-config:/home/paperbanana/.config/paperbanana-cn \
  -v paperbanana-cn-data:/home/paperbanana/.local/share/paperbanana-cn \
  -v paperbanana-cn-outputs:/work/outputs \
  ghcr.io/mituan-ai/paperbanana-cn:2.0.1 \
  studio --host 0.0.0.0
Permanent install, source setup, and optional providers

Install the command in a uv-managed environment:

bash
uv tool install paperbanana-cn
paperbanana-cn studio

Run the current source checkout:

bash
git clone https://github.com/mituan-ai/PaperBanana-CN.git
cd PaperBanana-CN
uv sync
uv run paperbanana-cn studio

The default package includes Studio, MCP, PDF input, OpenAI-compatible services, and Gemini.

Optional adapterInstall
AWS Bedrockuv tool install "paperbanana-cn[bedrock]"
Anthropicuv tool install "paperbanana-cn[anthropic]"
LiteLLMuv tool install "paperbanana-cn[litellm]"
All optional providersuv tool install "paperbanana-cn[all-providers]"

For CI, use paperbanana-cn connections add --api-key-env ENV_VAR so the key is read from an environment variable instead of a command-line value.

V1 and upstream

V2 is maintained on main as the paperbanana-cn distribution, the paperbanana_cn Python module, and the paperbanana-cn command.

  • Browse the frozen v1 branch
  • Download the v1.0.0 release

The scientific figure-generation core is based on llmsresearch/paperbanana. PaperBanana-CN is an unofficial community implementation and is not affiliated with or endorsed by the upstream authors.

Community

PaperBanana-CN is maintained by mituan under the MIT License.

  • Ask usage questions in Discussions.
  • Report reproducible bugs in Issues.
  • Report vulnerabilities through Private Vulnerability Reporting.
  • Read CONTRIBUTING.md before opening a pull request.
Development checks

Read the full README โ†’View source on GitHub โ†’

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Frequently Asked Questions about PaperBanana CN

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "paperbanana-cn": { "command": "npx", "args": ["-y", "PaperBanana-CN"] } }

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Technical Specs & Signals

Category๐Ÿ’ปDeveloper Tools
More technical detailsExpand โ–พ
TransportSTDIO
RuntimePython
Last updatedSep 7, 2026
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars79
GitHub Star CountTotal stargazers on GitHub representing community popularity (79 stars).
41Quality signal: Fair ยท 41/100How this signal is calculated โ–พ
Server availabilityNot measured

Not scored for repo-hosted servers โ€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools16/30
Adoption & activity5/15
Community engagement0/10

A guidance signal from public completeness & health data โ€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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