CrawlForge MCP vs Daloopa: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CrawlForge MCP and Daloopa — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CrawlForge MCP
CrawlForge
MCP server giving AI agents 31 web scraping, search and extraction tools that return clean markdown or structured JSON.
Key features
- 31 MCP-Native Tools: Fetch, scrape, extract, search and research tools exposed through one MCP server that plugs into Claude, Cursor and LangChain.
- Clean Markdown and Structured JSON: Converts any URL into AI-ready markdown or schema-defined JSON in a single call.
- Site Mapping and Deep Crawling: map_site and crawl_deep discover and follow internal links across entire websites.
- Batch Scraping: Process hundreds of URLs in parallel with batch_scrape and retrieve paginated results.
- Browser Sessions and Actions: Keep a page alive across calls and scrape pages that need clicks, scrolls or form fills.
- Search and Research: search_web, reddit_search and deep research tools return ranked results and multi-source findings.
- Change Tracking: track_changes monitors pages with baselines, schedules and alerts.
- Credit-Based Metering: Each tool costs 1-5+ credits per call, so usage and cost stay transparent.
Best for
- Agent Web Access: Giving a coding or research agent live web data through one MCP server.
- RAG Ingestion: Converting documentation sites into clean markdown for retrieval pipelines.
- Competitor Monitoring: Tracking pricing or content changes on competitor pages with alerts.
- SEO Analysis: Checking where a domain ranks for keywords and generating llms.txt files.
- Lead and Market Research: Extracting structured data from product, directory and social pages at scale.
- Document Processing: Pulling text out of PDFs and other documents found online.
Daloopa
Daloopa
A financial-modeling copilot and fundamental-data provider that populates Excel and LLM workflows with structured public-company financials and KPIs.
Key features
- Structured Fundamentals Extraction: Automatically extracts and normalizes financial statements and operational KPIs (income statement, balance sheet, cash flow, and custom metrics) from SEC filings, investor presentations, and PDFs into structured time-series formats.
- Excel Model Integration: Populates and updates users' native Excel models with source-linked fundamental data and formulas, enabling one-click refreshes of models after earnings or data updates while preserving model structure.
- LLM & MCP Connectors: Provides connectors and an HTTP API (used as an MCP/resource in platforms like Claude) so LLMs can query high-quality fundamentals and KPIs with source citations and integrate data into natural-language workflows.
- Large Coverage Universe: Maintains coverage of thousands of public companies (noted as 3,500+ in partner docs), including historical quarter and fiscal-year time series and specialized operational metrics for sector-specific analysis.
- Document-to-Spreadsheet Automation: Converts data from unstructured documents (CIMs, pitchbooks, investor decks) into clean Excel tables and time series to accelerate due diligence and model-building.
- Embeddable Widget & Developer Tools: Offers embeddable demo widgets and developer examples (GitHub repos) to streamline integration into internal apps, portals, or research tools for interactive data access.
- Auditability & Source Linking: Every data point links back to the original filing or document, enabling verification, transparent audit trails, and defensible research outputs.
- Programmatic access to fundamentals, financial statements, and operational KPIs (cited to SEC filings and investor materials)
- Coverage of thousands of public companies (documented as 3,500+ in integrated product docs)
- Model Context Protocol (MCP) / HTTP connector support for integration with LLMs (example: Claude MCP HTTP transport)
- Embeddable widget with demo code (GitHub repo) for web embedding
- Excel integration to push/update data directly into user spreadsheets and models
- Document extraction: parse PDFs, CIMs, investor decks into structured Excel-compatible outputs
- Time series data and quarter-level KPI histories for multi-period analysis
- Server-side authentication token handling recommended for embed/API usage
- Provides audit trails and citation metadata for sourced data
- Non-real-time (post-earnings) data updates; not positioned as intraday real-time feed
Best for
- Automated Model Refreshes: Updating multi-sheet Excel financial models automatically after quarterly earnings releases, preserving formulas and assumptions while refreshing underlying fundamentals.
- LLM-Powered Financial Queries: Connecting Daloopa to LLMs (via MCP or API) so analysts can ask natural-language questions about KPIs, run time-series comparisons, and receive answers with source citations.
- Due Diligence & Document Extraction: Extracting financial schedules and metrics from acquisition CIMs, investor decks, or PDFs into structured spreadsheets to accelerate M&A or credit diligence.
- Peer Benchmarking and Screening: Pulling standardized metrics across a set of 3–10 peer companies to compute relative performance, growth rates, and operational efficiency comparisons for investment memos.
- Portfolio Monitoring & Reporting: Feeding up-to-date fundamentals into portfolio dashboards to monitor positions, calculate valuation metrics (DCF inputs, multiples), and generate audit-ready reports.
- Model Building & Validation: Generating clean starter models (DCF, LBO, comparables) from extracted data and validating assumptions by comparing extracted time series against user models.
- Automate updating and ramping of Excel financial models with authoritative fundamentals
- Enable LLMs to query structured financial data and KPIs with source citations
- Extract structured financial tables from SEC filings and offering documents into Excel
- Benchmark and time-series analysis across peer companies for investment research
- Due diligence workflows that require consolidated, cited company financials
- Generate spreadsheet formulas and rebuild model structure from raw filings