gasoline-agentic-browser-devtools-mcp: Gasoline Agentic Browser MCP — Browser observability for AI coding assistants

by brennhill · MCP Server · ★ 54

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

Security audit verdict: SAFE · quality 70/100

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.

agentic-aiagentic-workflowautomationbrowser-automationbrowser-extensiondevtoolsdevtools-extensionmcpquality-assurance

Quick Facts

Stars54
Forks2
LanguageGo
CategoryMCP Server
LicenseAGPL-3.0
Quality Score70/100
Open Issues45
Last Updated2026-03-07
Created2026-01-23
Platformsbrowser, claude-code, go, mcp
Est. Tokens~10413k

About gasoline-agentic-browser-devtools-mcp

## Branch Policy (Read First) If you want something working, load code and run the server from . makes zero promises on regressions or issues and is treated as work in progress. Stable builds are compressed, tagged, and moved to . [![Codacy Badge](https://app.codacy.com/project/badge/Grade/62158fcb044348c3bc519

Compatible Skills

These tools work well together with gasoline-agentic-browser-devtools-mcp for enhanced workflows:

  • gemini-browser-agent — semantic(0.41)+complementary+rare_topics+similar_pop+shared_platform (54%)
  • ai-observer — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • canvas — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)

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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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