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Paperzilla is designed for both human reading and programmatic use. This guide focuses on Paperzilla as a data layer for agents.

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Available interfaces

Use these depending on your workflow:
  • CLI (pz) for shell/script execution
  • MCP (/api/mcp) for LLM tool-calling workflows
  • RSS/Atom for polling in feed infrastructure
See CLI guide, MCP integration, Claude guide, and RSS/Atom guide.
  1. Create and tune projects in the app.
  2. Authenticate CLI with pz login.
  3. Use pz project list to get the project ID you need.
  4. Use pz feed <project-id> --json for browse-style automation, or pz feed search --project-id <id> --query <q> --json for text search across the full feed.
  5. Add filters (--must-read, --since, --limit, --feedback-filter) based on your workflow.

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OpenClaw skill

The Paperzilla CLI is published as an OpenClaw skill on ClawHub. Install it with openclaw skills install paperzilla to let OpenClaw use pz commands directly. If you already use the separate ClawHub CLI, clawhub install paperzilla also works. See Use the Paperzilla CLI with OpenClaw for the full setup flow.

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Data surfaces you can combine

  • App feed for visual review
  • CLI for structured automation
  • RSS/Atom for polling in feed infrastructure
  • Email digests for human review cadence

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MCP tool surface

Current tools:
  • projects_list
  • projects_get
  • feed_get
  • feed_search
  • feed_atom_url
  • paper_get
  • paper_markdown
Optional prompt:
  • feed_title_filter
Use feed_search when your workflow needs text search across an entire project feed. It is the MCP path for search by title, author, abstract, or summary terms. Setup details: MCP integration.

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Example workflows

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Why this matters

If your workflow depends on “fresh papers + relevance filtering + automation,” treat Paperzilla projects as the persistent source and let agents consume that data layer.