info-research-report

Automate government-style research report generation by gathering topics, extracting multi-source web results, and generating structured DOCX reports with cover, metadata, background, methods, conclusions, and sources. Optional browser automation and LLM-based summarization streamline the end-to-end workflow.

Generating structured DOCX research reports requires python-docx for document creation, requests for data gathering, and BeautifulSoup for web scraping and text extraction. Optional integrations include mcporter and browseros-mcp for live data collection and browser automation.

Yes, you can skip web scraping to speed up DOCX report generation by using the --no-fetch option. This bypasses live data collection and allows the workflow to process existing data or generate reports directly from provided inputs without browser automation.

Yes, the research reporting workflow supports LLM summarization for policy briefings and regulatory inquiries. Environment variable configuration enables LLM-based summarization to condense multi-source information into structured DOCX reports for official government use.

Structure multi-source information into formal DOCX reports by automatically organizing extracted data into cover, metadata, background, methodology, conclusions, and source modules. This structured approach suits research projects, policy briefings, and regulatory inquiries requiring official government formatting.

BeautifulSoup and requests handle static web scraping for report generation but may not capture dynamic JavaScript-rendered content. For live data collection from dynamic pages, optional browser automation tools like browseros-mcp provide enhanced extraction capabilities beyond standard HTTP requests.