jw-org-mcp
An MCP server that provides controlled, verifiable access to official jw.org content, enabling AI applications to search articles, retrieve full articles, and lookup scriptures without hallucinations.
README
JW.Org MCP Tool
A Model Context Protocol (MCP) server that provides controlled, verifiable access to content from jw.org for AI applications and LLM integrations.
Overview
The JW.Org MCP Tool ensures that scriptural and doctrinal information comes exclusively from official jw.org sources, eliminating the risk of hallucinations or external contamination when handling religious queries. This tool acts as a trusted intermediary between AI applications and jw.org content.
Features
- Trusted Source Enforcement: Fetches data strictly from jw.org domains
- Comprehensive Search: Search across articles, videos, publications, audio, and scriptures
- Intelligent Query Parsing: Extracts meaningful search terms from natural language queries
- Full Article Retrieval: Get complete article content with scripture references
- Scripture Lookup: Direct scripture reference search
- Performance Optimized: 15-minute caching, Brotli compression, async operations
- Structured Output: Machine-readable responses with verification metadata
Installation
Requirements
- Python 3.13+
- uv for package management
Install with uv
# Clone the repository
git clone https://github.com/Bjern/jw-org-mcp.git
cd jw-org-mcp
# Install dependencies
uv sync
# Install with development dependencies
uv sync --group dev
Usage
Running the MCP Server
uv run jw-org-mcp
The server runs in stdio mode and communicates via the Model Context Protocol.
Adding to Claude Desktop
To use this MCP server with Claude Desktop, add it to your Claude configuration file:
Location:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
Configuration:
{
"mcpServers": {
"jw-org": {
"command": "uv",
"args": [
"--directory",
"E:\\Projects\\Python\\jw-org-mcp",
"run",
"jw-org-mcp"
]
}
}
}
Note: Replace E:\\Projects\\Python\\jw-org-mcp with the actual path to your project directory. On Windows, use double backslashes (\\) in the path.
WINDOWS -> FEB 2026 -> If you are using this as a custom connector MCP tool, then, you might find that the Claude Desktop app on Windows is not working properly. The app does not launch, etc...
This is because, there is a bug that the new app (Since Feb 2026) has changed it's default folder to the MSIX virtualized path:
C:\Users{username}\AppData\Local\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude\claude_desktop_config.json
Or paste %localappdata%\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude in the windows Run dialog.
After saving the configuration:
- Restart Claude Desktop
- The JW.Org MCP tools will be available in your conversations
- Look for tools like
search_content,get_article, andget_scripture
Configuration
Configuration is done via environment variables with the prefix JWORG_MCP_:
# Cache settings
export JWORG_MCP_CACHE_TTL_SECONDS=900 # 15 minutes (default)
export JWORG_MCP_ENABLE_CACHE=true
# Request settings
export JWORG_MCP_REQUEST_TIMEOUT=30
export JWORG_MCP_MAX_RETRIES=3
# Search settings
export JWORG_MCP_DEFAULT_LANGUAGE=E # English
export JWORG_MCP_DEFAULT_SEARCH_LIMIT=10
# Logging
export JWORG_MCP_LOG_LEVEL=INFO
MCP Tools
search_content
Search JW.Org content across multiple types.
Parameters:
query(required): Search query - can be natural languagefilter(optional): Content type -all,publications,videos,audio,bible,indexes(default:all)language(optional): Language code -Efor English,Sfor Spanish, etc. (default:E)limit(optional): Maximum results (default: 10)
Example:
{
"query": "What does the Bible say about love?",
"filter": "all",
"limit": 5
}
The query parser automatically extracts "love" as the search term.
get_article
Retrieve full article content from a jw.org URL. Supports both direct article URLs and publication finder URLs.
When given a publication-level URL (e.g., a magazine issue), the tool returns a table of contents listing individual articles with their direct URLs, which can then be fetched individually.
Parameters:
url(required): Article URL from wol.jw.org or a publication finder URL
Example:
{
"url": "https://wol.jw.org/en/wol/d/r1/lp-e/1985720"
}
get_scripture
Get scripture text by reference.
Parameters:
reference(required): Scripture reference (e.g., "John 3:16", "1 Thessalonians 5:3")translation(optional): Bible translation code (default: "nwtsty")
Example:
{
"reference": "John 3:16"
}
get_cache_stats
Get cache statistics including hit rate and entry count.
Parameters: None
Development
Setup Development Environment
# Install with development dependencies
uv sync --group dev
# Install pre-commit hooks (optional)
uv run pre-commit install
Running Tests
# Run all tests
uv run pytest
# Run with coverage
uv run pytest --cov=jw_org_mcp --cov-report=html
# Run specific test file
uv run pytest tests/test_parser.py
Code Quality
# Run linter
uv run ruff check .
# Format code
uv run ruff format .
# Type checking
uv run mypy src/
# Security scan
uv run bandit -r src/ -c pyproject.toml
Project Structure
jw-org-mcp/
├── .github/
│ └── workflows/
│ └── tests.yml # CI pipeline (lint, type check, security, tests)
├── src/
│ └── jw_org_mcp/
│ ├── __init__.py # Entry point
│ ├── auth.py # Authentication & CDN discovery
│ ├── cache.py # Caching layer
│ ├── client.py # JW.Org API client
│ ├── config.py # Configuration management
│ ├── exceptions.py # Custom exceptions
│ ├── models.py # Data models
│ ├── parser.py # Content parsers
│ └── server.py # MCP server implementation
├── tests/ # Test suite
├── docs/ # Documentation
├── pyproject.toml # Project configuration
└── README.md
Architecture
Authentication Flow
- Discover CDN URL from jw.org homepage
- Request JWT token from CDN endpoint
- Use token for authenticated API requests
- Automatically refresh token before expiration
Search Flow
- Parse user query to extract search terms
- Check cache for existing results
- Make authenticated API request if cache miss
- Parse and structure response
- Cache results for 15 minutes
- Return structured data
Content Retrieval
- Fetch HTML content from wol.jw.org
- If the page is a publication index (table of contents), extract article links and return them
- Otherwise, parse article structure (title, paragraphs, references)
- Extract clean text without HTML artifacts
- Cache parsed content
- Return structured article data
API Response Format
All responses include metadata for verification:
{
"data": {
// Response-specific data
},
"metadata": {
"source_domain": "jw.org",
"source_url": "https://...",
"timestamp": "2024-01-01T00:00:00Z",
"query_params": {},
"cache_hit": false
}
}
Performance
- Response Time: < 2 seconds for search queries (cached: < 100ms)
- Cache TTL: 15 minutes (configurable)
- Compression: Brotli for all API requests
- Concurrency: Async I/O with connection pooling
Error Handling
The tool provides graceful error handling with specific exception types:
AuthenticationError: JWT token issuesCDNDiscoveryError: CDN discovery failuresSearchError: Search operation failuresContentRetrievalError: Content fetch failuresParseError: Content parsing failures
All errors are logged and returned with descriptive messages.
Security & Privacy
- No PII Logging: No personally identifiable information is logged
- HTTPS Only: All external requests use HTTPS
- Token Security: JWT tokens are managed securely in memory
- Input Validation: All user inputs are sanitized
Contributing
- Fork the repository
- Create a feature branch
- Make your changes with tests
- Ensure all tests pass and code is formatted
- Submit a pull request
License
This project is licensed under the GNU General Public License v3.0.
Support
For issues and questions:
- GitHub Issues: https://github.com/Bjern/jw-org-mcp/issues
- Documentation: See
docs/folder
Acknowledgments
- Built with FastMCP
- Uses the Model Context Protocol standard
- Provides verified access to jw.org content
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。