web-agent-protocol: Web Agent Protocol (WAP) - Record and replay user interactions in the browser with MCP support

by OTA-Tech-AI · MCP Server · ★ 509

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Featured in: browser-use, Playwright MCP & AI Browser Agents

Security audit verdict: SAFE · quality 71/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsai-toolsbrowser-automationbrowser-usellmmcpmcp-servermodelcontextprotocolplaywrightpython

Quick Facts

Stars509
Forks90
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score71/100
Open Issues11
Last Updated2025-06-19
Created2025-05-17
Platformsbrowser, mcp, python
Est. Tokens~364k

About web-agent-protocol

Web Agent Protocol Overview The Web Agent Protocol (WAP) is a standardized framework designed to enable seamless interaction between users, web agents, and browsers by recording and replaying browser actions. It separates the concerns of action recording and execution, allowing for efficient automation and reusability.

Compatible Skills

These tools work well together with web-agent-protocol for enhanced workflows:

  • browser-use — semantic(0.42)+complementary+rare_topics+same_lang+shared_platform (64%)
  • Clawome — semantic(0.28)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)
  • browserable — semantic(0.44)+complementary+rare_topics+similar_pop+shared_platform (59%)
  • LiteWebAgent — semantic(0.26)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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