PluginBench
MCP Server
Active
MIT

PaperBanana-CN MCP Server

io.github.mituan-ai/paperbanana-cn

Generate academic diagrams and statistical plots with independent VLM and image model connections.

What is the PaperBanana-CN MCP server?

PaperBanana-CN is an MCP server that generates methodology diagrams from research descriptions and statistical plots from CSV or JSON data. It features separate VLM and image-service connections, Chinese and English interfaces, and explicit aspect-ratio and resolution controls for scientific figure creation.

PaperBanana-CN automates the creation of publication-ready scientific figures. It generates methodology diagrams by converting research descriptions into visual workflows, and creates statistical plots from data files. The server supports independent model connections for vision and image generation, making it flexible for different API providers and use cases in academic research.

How to install PaperBanana-CN

Copy-paste configuration for popular MCP clients.

transport: stdio
Config generated by PluginBench — verify against the source before use.
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "paperbanana-cn": {
      "command": "uvx",
      "args": [
        "paperbanana-cn",
        "mcp"
      ]
    }
  }
}

Tools & capabilities

Tools this server exposes to the agent.

  • Generate methodology diagram — Create methodology diagrams from research descriptions with configurable aspect ratios (1:1, 4:3, 3:2, 5:4, 16:9, 21:9, 4:5, 3:4, 2:3, 9:16), resolutions (1K, 2K, 4K), and output formats
  • Generate statistical plot — Create statistical plots from CSV or JSON data using VLM analysis
  • Continue saved run — Improve or iterate on previously saved figure generation runs
  • Quality evaluation — Evaluate and assess the quality of generated figures using VLM
  • Full-paper orchestration — Automate figure generation across an entire research paper
  • Batch generation — Generate multiple figures in batch mode
  • Parameter sweep — Systematically test different parameters for figure generation
  • Multi-panel composition — Combine multiple figures into multi-panel layouts
  • Run browser — Browse and manage previously generated runs and figures

Use cases

  • Generate methodology diagrams for research papers by describing your approach and selecting desired aspect ratio and resolution
  • Create statistical plots from experimental data in CSV or JSON format to visualize results
  • Batch generate multiple figures for a paper with consistent styling and formatting
  • Evaluate and iterate on figure quality using AI-powered assessment
  • Compose multi-panel figures combining methodology diagrams and statistical plots for comprehensive paper illustrations

PaperBanana-CN MCP server FAQ

What is PaperBanana-CN?

PaperBanana-CN generates publication-ready scientific figures including methodology diagrams and statistical plots. It uses separate VLM and image generation model connections, supports multiple aspect ratios and resolutions, and provides both a Studio interface and MCP server for integration with AI tools.

Is PaperBanana-CN free?

PaperBanana-CN itself is free and open-source under the MIT License. However, you need API access to VLM and image generation services (OpenAI, Gemini, Anthropic, AWS Bedrock, or compatible services), which may incur costs depending on your provider.

How do I install PaperBanana-CN in Cursor or Claude?

Add PaperBanana-CN as an MCP server in your client configuration with: {"command": "uvx", "args": ["paperbanana-cn", "mcp"]}. The server provides 11 tools for figure generation and management. You need Python 3.10-3.12 and uv installed.

What model connections are required?

For methodology diagrams you need both a VLM (vision language model) and image generation service. For statistical plots, only a VLM is required. Multi-panel composition and run browsing require no model connections. Each connection has independent protocol, Base URL, API key, model name, and timeout settings.

What data formats does it support?

Statistical plots can be generated from CSV or JSON data files. Methodology diagrams are created from text descriptions. The server also supports PDF input for paper processing and can output figures in multiple formats.

What are the supported aspect ratios and resolutions?

Aspect ratios: 1:1, 4:3, 3:2, 5:4, 16:9, 21:9, 4:5, 3:4, 2:3, 9:16. Resolution tiers: 1K, 2K, 4K. The interface validates that your selected combination is supported by your configured image generation service.

README (reference)

Source of truth, from the repository.

<!-- mcp-name: io.github.mituan-ai/paperbanana-cn --> <p align="right"> <strong>English</strong> · <a href="https://github.com/mituan-ai/PaperBanana-CN/blob/main/README_CN.md">简体中文</a> </p> <p align="center"> <img src="https://raw.githubusercontent.com/mituan-ai/PaperBanana-CN/main/assets/readme/hero.webp" width="100%" alt="PaperBanana-CN scientific figure workbench with a generated multimodal fault-diagnosis figure" > </p> <p align="center"> 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. </p> <p align="center"> <a href="#quick-start"><img src="https://img.shields.io/badge/LAUNCH_STUDIO-uvx_paperbanana--cn_studio-147862?style=for-the-badge&logo=gnometerminal&logoColor=white" alt="Launch Studio"></a> <a href="https://pypi.org/project/paperbanana-cn/"><img src="https://img.shields.io/badge/INSTALL-PYPI-3775A9?style=for-the-badge&logo=pypi&logoColor=white" alt="Install from PyPI"></a> </p> <p align="center"> <a href="https://github.com/mituan-ai/PaperBanana-CN/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/mituan-ai/PaperBanana-CN/ci.yml?branch=main&style=flat-square&logo=githubactions&logoColor=white&label=CI" alt="CI status"></a> <img src="https://img.shields.io/badge/Package-2.0.1-147862?style=flat-square" alt="Package version 2.0.1"> <a href="https://github.com/mituan-ai/PaperBanana-CN/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-52605B?style=flat-square" alt="MIT License"></a> </p>

One run from input to figure

<p align="center"> <img src="https://raw.githubusercontent.com/mituan-ai/PaperBanana-CN/main/assets/readme/studio-workflow.gif" width="960" alt="A PaperBanana-CN Studio run progressing from configured inputs to a completed scientific figure" > </p>

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

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 → 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.

<details> <summary><strong>Connection manager screenshot and protocol notes</strong></summary> <br> <p align="center"> <img src="https://raw.githubusercontent.com/mituan-ai/PaperBanana-CN/main/assets/readme/connections-zh.webp" width="100%" alt="PaperBanana-CN connection manager with separate fields for a VLM service" > </p>

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

</details>

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:

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 · 4:3 · 3:2 · 5:4 · 16:9 · 21:9 · 4:5 · 3:4 · 2:3 · 9:16

Resolution tiers:

1K · 2K · 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
<p align="center"> <img src="https://raw.githubusercontent.com/mituan-ai/PaperBanana-CN/main/assets/readme/studio-methodology-en.webp" width="100%" alt="PaperBanana-CN methodology-diagram workspace with task inputs and a completed result" > </p> <p align="center"><sub>Methodology-diagram workspace</sub></p> <p align="center"> <img src="https://raw.githubusercontent.com/mituan-ai/PaperBanana-CN/main/assets/readme/studio-statistical-plot-en.webp" width="100%" alt="PaperBanana-CN statistical-plot workspace with CSV input and a completed line chart" > </p> <p align="center"><sub>Statistical-plot workspace using synthetic demonstration data</sub></p>

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
<details> <summary><strong>MCP client configuration</strong></summary>
{
  "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.

</details> <details> <summary><strong>Docker</strong></summary>
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
</details> <details> <summary><strong>Permanent install, source setup, and optional providers</strong></summary>

Install the command in a uv-managed environment:

uv tool install paperbanana-cn
paperbanana-cn studio

Run the current source checkout:

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.

</details>

V1 and upstream

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

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.

<details> <summary><strong>Development checks</strong></summary>
git clone https://github.com/mituan-ai/PaperBanana-CN.git
cd PaperBanana-CN
uv sync --extra dev
uv run pytest tests/ -q
uv run ruff check paperbanana_cn/ mcp_server/ tests/ scripts/

Do not upload API keys, private relay URLs, unpublished papers, private datasets, local connection stores, or generated run directories.

</details>

Star history

<a href="https://www.star-history.com/?repos=mituan-ai%2FPaperBanana-CN&type=date&legend=top-left"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=mituan-ai/PaperBanana-CN&type=date&theme=dark&legend=top-left&sealed_token=tONDl7QT6gBodlxbICyg-BGsu060cE2rb7tZmOubJS6r7ZQMt8tGi9pUE274ujDrVgxHmy3U6QwUFtqtCDbU5abOpd8t9gKCK6B48Typy5z9FLLBvnF4uA" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=mituan-ai/PaperBanana-CN&type=date&legend=top-left&sealed_token=tONDl7QT6gBodlxbICyg-BGsu060cE2rb7tZmOubJS6r7ZQMt8tGi9pUE274ujDrVgxHmy3U6QwUFtqtCDbU5abOpd8t9gKCK6B48Typy5z9FLLBvnF4uA" /> <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=mituan-ai/PaperBanana-CN&type=date&legend=top-left&sealed_token=tONDl7QT6gBodlxbICyg-BGsu060cE2rb7tZmOubJS6r7ZQMt8tGi9pUE274ujDrVgxHmy3U6QwUFtqtCDbU5abOpd8t9gKCK6B48Typy5z9FLLBvnF4uA" /> </picture> </a>

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