What is Docreadermcp

DocReaderMCP is an MCP server designed to read and analyze online documents, enabling AI assistants to solve problems by extracting and synthesizing information from various web-based sources.

Use cases

Use cases for DocReaderMCP include assisting users in finding specific information in technical documentation, summarizing lengthy articles, and providing quick answers to questions based on online resources.

How to use

To use DocReaderMCP, clone the repository, install the required dependencies, and run the script directly or via the fastmcp CLI. You can also integrate it into the Cursor interface by adding it as a local MCP tool.

Key features

Key features include searching for relevant pages across documentation websites, extracting content from specific pages, aggregating and summarizing discovered information, and completing the document Q&A workflow in a single step.

Where to use

DocReaderMCP can be used in various fields such as research, education, technical support, and any domain where information retrieval from online documents is necessary.

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:

pip install fastmcp beautifulsoup4 requests openai python-dotenv

1. Create a .env file and add your API key, preferably a NetMind API key:

API_KEY=your_api_key_here

Usage

Run Directly

cd path/to/DocReaderMCP
python DocReader.py

Run with fastmcp CLI

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

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:

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)