CrawlForge MCP vs DCP - The permission layer for AI agents: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CrawlForge MCP and DCP - The permission layer for AI agents — 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.
DCP - The permission layer for AI agents
DCP (maintained by 1lystore and the DCP community)
Non-custodial permission layer that lets AI agents request vault actions while keeping keys and credentials on your device.
Key features
- Vault Permission Proxy: Exposes vault-held secrets and credentials as permissioned actions via MCP so agents can request operations without ever reading raw keys or .env files.
- Wallet Signing & Transaction Approval: Enables agents to request wallet signing (e.g., Solana) with user confirmation, keeping private keys local and non-custodial.
- Human-in-the-Loop Approvals: Desktop and mobile-driven approval flows let users approve, deny, or require manual confirmation for sensitive actions with a single tap.
- Budgets & Revocation: Configure budgets or usage limits per agent and revoke permissions dynamically to limit exposure and control ongoing agent behavior.
- Remote Pairing & Agent Deployment: Create remote invites from the Desktop app to pair VPS-hosted agents; installer can configure systemd services and automatically integrate with OpenClaw or Hermes.
- MCP Integration: Implements permission boundaries over the Model Context Protocol so multiple agent frameworks (Claude, Cursor, Hermes, OpenClaw) can interoperate with DCP.
- Agent-Safe Workflows: Prevents secrets from entering agent configuration by routing API calls and secret access through DCP, reducing credential leakage risk.
- Permission boundary for agents: grants capabilities (not raw keys) to agents via requests and approvals
- Vault and API credential access with human approval, deny, budget, and revoke controls
- Wallet signing support (example: Solana wallet address retrieval and signing flows)
- Integration with MCP so agents (Claude Desktop, Cursor, OpenClaw, Hermes, custom MCP agents) can request actions
- Desktop application for local GUI setup and invite generation
- Remote agent installer workflow that pairs VPS hosts, installs a systemd service, and auto-configures supported agents
- Automatic or manual Hermes integration (host-native config ~/.hermes/config.yaml or Docker config /opt/data/config.yaml)
- Audit trails and cryptographically verifiable artifacts via the DCP-AI protocol stack
- Ecosystem SDKs and tools (npm packages, PyPI package, WASM, CLI, Rust crates, Go reference, Docker images)
- Quickstart examples for local and remote agents to request protected data
Best for
- Secure wallet operations: Allow Claude Desktop or another agent to sign Solana transactions only after a user approves each request, without exposing private keys.
- Remote agent automation: Deploy an agent on a VPS (OpenClaw/Hermes) and pair it with DCP Desktop to grant scoped, auditable access to specific credentials for automated tasks.
- Secrets-free development: Developers test and run agent workflows locally or in CI without embedding API keys in .env files by proxying requests through DCP.
- Human-gated automation: Build automations where an agent proposes actions (banking, deployments, privileged API calls) and a human reviews and approves before execution.
- Enterprise access control: Set per-agent budgets, fine-grained permissions, and revocation policies to limit blast radius when agents access corporate APIs or data stores.
- Audit and compliance: Maintain a tamper-evident record of agent requests and human approvals for post-hoc review and regulatory compliance.
- Allowing an LLM agent to sign blockchain transactions or retrieve a blockchain wallet address without exposing private keys
- Granting a remote agent running on a VPS permission to use API credentials under human-approved budgets
- Pairing and managing remote autonomous agents (OpenClaw, Hermes) with a single-click desktop invite and systemd installer
- Enforcing per-action human approvals and budgets for agents that perform sensitive operations
- Auditing and verifying agent actions using DCP-AI cryptographic artifacts and SDKs for downstream verifiers