MedAdapt Content Server
A specialized MCP server for Claude Desktop that enhances AI-assisted medical learning by fetching and processing educational resources from PubMed, NCBI Bookshelf, and user-provided documents.
README
MedAdapt Content Server
A specialized Model Context Protocol (MCP) server for Claude Desktop that enhances AI-assisted medical learning by fetching and processing educational resources from PubMed, NCBI Bookshelf, and user-provided documents.
Overview
The MedAdapt Content Server integrates with Claude Desktop to provide tools for searching, retrieving, and analyzing medical education content. It serves as a bridge between Claude and medical knowledge sources, allowing for enhanced AI-assisted learning experiences.
Quick Start
# Clone the repository
git clone https://github.com/ryoureddy/medadapt-content-server.git
cd medadapt-content-server
# Install dependencies
pip install -r requirements.txt
# Run the server
python content_server.py
Features
- Content Search: Search for medical educational content across multiple sources
- Resource Retrieval: Fetch complete articles, book chapters, and user documents
- Topic Overviews: Generate comprehensive overviews of medical topics
- Learning Resources: Suggest appropriate learning resources based on topic and student level
- Learning Plans: Create structured learning plans with objectives and resources
- Content Analysis: Extract key points, methodologies, and findings from medical resources
- User Content: Import and analyze user-provided documents
Installation
Standard Installation
- Clone the repository:
git clone https://github.com/ryoureddy/medadapt-content-server.git
cd medadapt-content-server
- Create a virtual environment (optional but recommended):
python -m venv .venv
source .venv/bin/activate # On Windows, use: .venv\Scripts\activate
- Install dependencies:
pip install -r requirements.txt
- Configure (optional):
- Get an NCBI API key for improved rate limits: https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/
- Create a
.envfile based on.env.example
Usage
Running the Server
python content_server.py
Integration with Claude Desktop
- Open Claude Desktop
- Go to Settings → Model Context Protocol → Add Server
- Configure with the following JSON in your
claude_desktop_config.jsonfile located in:- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
- macOS:
{
"mcpServers": {
"medadapt": {
"command": "/path/to/python",
"args": [
"/path/to/medadapt-content-server/content_server.py"
],
"env": {
"DB_PATH": "/path/to/medadapt-content-server/medadapt_content.db"
}
}
}
}
Replace /path/to/python with your actual Python path (e.g., /opt/anaconda3/bin/python or C:\Python311\python.exe).
Replace /path/to/medadapt-content-server/ with the absolute path to your cloned repository.
Important: The
DB_PATHenvironment variable ensures the database file is created and accessed with an absolute path, preventing common file access errors.
Populating Initial Topic Mappings
python populate_topics.py
Testing
Run tests to verify everything is working:
python test_server.py
Example Usage with Claude
Scenario 1: Learning About a Medical Topic
User prompt to Claude:
I'd like to learn about the cardiac cycle. Can you provide a big picture overview and help me understand the key concepts?
Scenario 2: Finding Specific Resources
User prompt to Claude:
I need to find recent research articles about COVID-19 treatment options. Can you help me find relevant resources?
Scenario 3: Creating a Learning Plan
User prompt to Claude:
I'm a second-year medical student studying neurology. Can you create a learning plan for understanding stroke pathophysiology?
Available Tools
The server provides the following tools to Claude:
search_medical_content: Search for medical content with filtersget_resource_content: Retrieve complete content for a specific resourceget_topic_overview: Generate comprehensive overview of a medical topicsuggest_learning_resources: Get personalized resource recommendationsimport_user_document: Upload user-provided learning materialsgenerate_learning_plan: Create structured learning plan with objectivesextract_article_key_points: Extract key findings from medical articles
Troubleshooting
Common Issues and Solutions
-
Database Connection Error
- Symptom:
sqlite3.OperationalError: unable to open database file - Solution: Make sure the
DB_PATHenvironment variable is set correctly in your Claude Desktop configuration, pointing to an absolute path where the application has write permissions.
- Symptom:
-
File Path Error
- Symptom:
No such file or directoryerrors - Solution: Ensure all paths in the Claude Desktop configuration are absolute paths without extra quotes or escape characters.
- Symptom:
-
API Rate Limiting
- Symptom: Slow or failed responses from PubMed or NCBI Bookshelf
- Solution: Get an NCBI API key and add it to your
.envfile
-
Claude Desktop Connection
- Symptom: Claude cannot connect to the MCP server
- Solution: Verify the server is running in a terminal window and properly configured in Claude Desktop
Project Structure
medadapt-content-server/
│
├── content_server.py # Main MCP server implementation
├── database.py # SQLite database interface
├── pubmed_utils.py # PubMed API utilities
├── bookshelf_utils.py # NCBI Bookshelf utilities
├── populate_topics.py # Script to populate initial topic data
├── test_server.py # Test script
├── requirements.txt # Python dependencies
├── .env.example # Example environment variables
└── README.md # Documentation
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- NCBI for providing access to PubMed and Bookshelf APIs
- Anthropic for Claude and the MCP integration capability
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。