# abcoder
Agentic backend coder - A Jupyter notebook manager with MCP (Model Context Protocol) integration for AI-assisted code execution and bioinformatics workflows.
**Supports multiple Jupyter kernels (Python, R, etc.) for parallel notebook management.**
## đĒŠ What can it do?
- **Jupyter Notebook Management**: Create, switch between, and manage multiple Jupyter notebooks
- **Multi-kernel Support**: Manage and run code in multiple Jupyter kernels (e.g., Python, R) simultaneously
- **Code Execution**: Execute single-step or multi-step code in Jupyter kernels
- **Variable Backup**: Safely backup variables before code execution to prevent data loss
- **API Documentation**: Query function and API documentation directly from the kernel
- **Output Handling**: Capture and display execution results, errors, and generated figures
- **Bioinformatics Integration**: Designed for bioinformatics workflows with support for common libraries like scanpy, pandas, numpy, etc.
## â Who is this for?
- **Bioinformaticians** who want AI assistance in their Jupyter workflows
- **AI developers** building agents that need to execute code in Jupyter environments
- **Researchers** who want to integrate AI tools with their computational notebooks
- **Anyone** who wants to use natural language to control Jupyter notebook execution
## đ Where to use it?
You can use abcoder in most AI clients, plugins, or agent frameworks that support the MCP:
- **AI clients**: Cherry Studio, Claude Desktop, etc.
- **Plugins**: Cline, etc.
- **Agent frameworks**: Agno, etc.
## đŦ Demo
A demo showing AI-assisted bioinformatics analysis in a Jupyter notebook using natural language commands through abcoder.
[](https://youtu.be/3jtXIeapslI)
**Click the image above to watch the demo video**
## đ Documentation
For complete documentation, visit: https://github.com/huang-sh/abcoder
## đī¸ Quickstart
### Install
Install from PyPI:
```bash
pip install abcoder
```
Test the installation:
```bash
abcoder run
```
### Configuration
#### Run abcoder locally
First, check the installation path:
```bash
which abcoder
# Example output: /home/user/bin/abcoder
```
Configure your MCP client:
```json
{
"mcpServers": {
"abcoder": {
"command": "/home/user/bin/abcoder",
"args": ["run"]
}
}
}
```
#### Run abcoder remotely
Start the server on your remote machine:
```bash
abcoder run --transport shttp --port 8000
```
Configure your local MCP client:
```json
{
"mcpServers": {
"abcoder": {
"url": "http://localhost:8000/mcp"
}
}
}
```
## đ ī¸ Available Tools
### Notebook Management
- `create_notebook`: Create a new Jupyter notebook with specified ID and path
- `switch_active_notebook`: Switch between different notebooks
### Code Execution
- `single_step_execute`: Execute a single code block
- `multi_step_execute`: Execute multiple code steps with cell addition
- `query_api_doc`: Query function documentation from the kernel
### Features
- **Variable Backup**: Automatically backup variables before execution
- **Error Handling**: Comprehensive error capture and reporting
- **Output Display**: Support for text, images, and other display data
- **Kernel Management**: Automatic kernel lifecycle management
## đ¤ Contributing
If you have any questions, welcome to submit an issue, or contact me(hsh-me@outlook.com). Contributions to the code are also welcome!
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"description": "# abcoder\n\nAgentic backend coder - A Jupyter notebook manager with MCP (Model Context Protocol) integration for AI-assisted code execution and bioinformatics workflows.\n\n**Supports multiple Jupyter kernels (Python, R, etc.) for parallel notebook management.**\n\n## \ud83e\udea9 What can it do?\n\n- **Jupyter Notebook Management**: Create, switch between, and manage multiple Jupyter notebooks\n- **Multi-kernel Support**: Manage and run code in multiple Jupyter kernels (e.g., Python, R) simultaneously\n- **Code Execution**: Execute single-step or multi-step code in Jupyter kernels\n- **Variable Backup**: Safely backup variables before code execution to prevent data loss\n- **API Documentation**: Query function and API documentation directly from the kernel\n- **Output Handling**: Capture and display execution results, errors, and generated figures\n- **Bioinformatics Integration**: Designed for bioinformatics workflows with support for common libraries like scanpy, pandas, numpy, etc.\n\n## \u2753 Who is this for?\n\n- **Bioinformaticians** who want AI assistance in their Jupyter workflows\n- **AI developers** building agents that need to execute code in Jupyter environments\n- **Researchers** who want to integrate AI tools with their computational notebooks\n- **Anyone** who wants to use natural language to control Jupyter notebook execution\n\n## \ud83c\udf10 Where to use it?\n\nYou can use abcoder in most AI clients, plugins, or agent frameworks that support the MCP:\n\n- **AI clients**: Cherry Studio, Claude Desktop, etc.\n- **Plugins**: Cline, etc.\n- **Agent frameworks**: Agno, etc.\n\n## \ud83c\udfac Demo\n\nA demo showing AI-assisted bioinformatics analysis in a Jupyter notebook using natural language commands through abcoder.\n\n[](https://youtu.be/3jtXIeapslI)\n\n**Click the image above to watch the demo video**\n\n## \ud83d\udcda Documentation\n\nFor complete documentation, visit: https://github.com/huang-sh/abcoder\n\n## \ud83c\udfce\ufe0f Quickstart\n\n### Install\n\nInstall from PyPI:\n```bash\npip install abcoder\n```\n\nTest the installation:\n```bash\nabcoder run\n```\n\n### Configuration\n\n#### Run abcoder locally\n\nFirst, check the installation path:\n```bash\nwhich abcoder\n# Example output: /home/user/bin/abcoder\n```\n\nConfigure your MCP client:\n```json\n{\n \"mcpServers\": {\n \"abcoder\": {\n \"command\": \"/home/user/bin/abcoder\",\n \"args\": [\"run\"]\n }\n }\n}\n```\n\n#### Run abcoder remotely\n\nStart the server on your remote machine:\n```bash\nabcoder run --transport shttp --port 8000\n```\n\nConfigure your local MCP client:\n```json\n{\n \"mcpServers\": {\n \"abcoder\": {\n \"url\": \"http://localhost:8000/mcp\"\n }\n }\n}\n```\n\n## \ud83d\udee0\ufe0f Available Tools\n\n### Notebook Management\n- `create_notebook`: Create a new Jupyter notebook with specified ID and path\n- `switch_active_notebook`: Switch between different notebooks\n\n### Code Execution\n- `single_step_execute`: Execute a single code block\n- `multi_step_execute`: Execute multiple code steps with cell addition\n- `query_api_doc`: Query function documentation from the kernel\n\n### Features\n- **Variable Backup**: Automatically backup variables before execution\n- **Error Handling**: Comprehensive error capture and reporting\n- **Output Display**: Support for text, images, and other display data\n- **Kernel Management**: Automatic kernel lifecycle management\n\n\n## \ud83e\udd1d Contributing\n\nIf you have any questions, welcome to submit an issue, or contact me(hsh-me@outlook.com). Contributions to the code are also welcome!",
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