DocReaderMCP

# DocReader MCP Tool DocReader is a powerful tool for reading and searching documents, built on the Model Context Protocol (MCP). It enables LLMs to search, extract, and synthesize information from web-based documents, assisting AI assistants in answering questions accordingly. ## Features - Search for relevant pages across documentation websites - Extract content from specific pages - Aggregate and summarize discovered information - Complete the document Q&A workflow in a single step ## Installation ### Requirements - Python 3.7 or higher - fastmcp - beautifulsoup4 - requests - openai - python-dotenv ### Installation Steps 1. Clone or download this repository. 2. Install the required dependencies: ```bash pip install fastmcp beautifulsoup4 requests openai python-dotenv ``` 1. Create a `.env` file and add your API key, preferably a [NetMind](https://www.netmind.ai) API key: ``` API_KEY=your_api_key_here ``` ## Usage ### Run Directly ```bash cd path/to/DocReaderMCP python DocReader.py ``` ### Run with fastmcp CLI ```bash cd path/to/DocReaderMCP fastmcp run DocReader.py ``` ### Using with Cursor #### Method 1: Temporary Addition 1. In the Cursor interface, click the extensions/plugins icon in the left sidebar. 2. Locate the MCP section or select "Add Tool". 3. Choose "Add Local MCP Tool". 4. Enter a tool name, such as "DocReader". 5. Select the execution method (either point to the script path or connect via URL). #### Method 2: Persistent Installation ```bash cd path/to/DocReaderMCP fastmcp install DocReader.py --name "DocReader" --with beautifulsoup4 requests openai python-dotenv ``` ## Toolset DocReader MCP provides the following tool functions: 1. **search_docs**: Search documentation pages to find those most relevant to your query. 2. **extract_content**: Extract content from a specified URL. 3. **summarize_findings**: Summarize the information collected. 4. **read_doc**: Complete the entire workflow—search, extraction, and summarization—in one step. ## Recommended Workflow 1. Start by using `search_docs` to find relevant pages on the documentation site. 2. Use `extract_content` to retrieve content from the most relevant pages. 3. Summarize your findings with `summarize_findings`. 4. Alternatively, use `read_doc` to perform all these steps at once. ## Example See `test_doc_reader.py` for more examples of how to use each tool function. A brief example: ```python from DocReader import search_docs, extract_content, summarize_findings, read_doc doc_url = "https://flax.readthedocs.io/en/latest/index.html" query = "How do I train a model with flax? Please help me write the training code and the inference code after training." # 1. Search for relevant pages results = search_docs(doc_url, query, depth=2, max_results=3) # 2. Extract content if results: page_content = extract_content(results[0]['url'], query) # 3. Summarize findings summary = summarize_findings(query) print(summary['summary']) # 4. One-step workflow final_answer = read_doc(doc_url, query) print(final_answer) ```