Browser testing on Playwright that checks the server, not just the screen, before it reports a pass.
MCPpedia last refreshed this data
io.github.HMAKT99/vedha is an MCP server that browser testing on Playwright that checks the server, not just the screen, before it reports a pass. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 62/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"io-github-hmakt99-vedha": {
"args": [
"-y",
"@vedhajs/mcp"
],
"command": "npx"
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
Browser testing on Playwright that checks the server, not just the screen, before it reports a pass.
This server supports HTTP transport. Be the first to test it - help the community know if it works.
Five weighted categories - click any category to see the underlying evidence.
No known CVEs.
Checked @vedhajs/mcp against OSV.dev.
Be the first to review
Have you used this server?
Share your experience - it helps other developers decide.
Sign in to write a review.
Others in browser
Open-source web scraper for AI agents with scrape, crawl, and map tools
Firecrawl, the context API for AI agents: search, scrape, interact with the web, plus paper search.
Search-first MCP server for JavaScript reverse engineering: 735 tools across 36 domains.
Extract data from any website with thousands of scrapers, crawlers, and automations on Apify Store ⚡
MCP Security Weekly
Get CVE alerts and security updates for io.github.HMAKT99/vedha and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
npm install -D @vedhajs/playwright-test
import { test, expect } from "@vedhajs/playwright-test"; // was "@playwright/test"

Run this demo: npx tsx examples/guard-demo/run.ts
The page says "Order placed", so the test passes. But the server answered POST /api/orders with a 500 and stored nothing. With the import changed, your tests run exactly as before, and a test whose own checks pass still fails when the app answered a request with a 5xx or a write failed outright. No rewrite, no new assertions. Details and options: guard mode. For CI, see examples/ci/.
An agent that trusts the screen makes the same mistake: it clicks "Save", sees "Saved" and reports success. Vedha's MCP server, CLI and SDK run on Playwright and check that each action really happened (page, network, and a read-back of stored data) before they report a pass.
| ~3x fewer missed bugs | when an AI agent tests: 9 seeded defects missed vs 26 for Playwright MCP (held-out sets 2-4) |
| 60 vs 0 | broken features caught with everyday text/URL checks (140 seeded defects, scripted) |
| 30% fewer tokens | for the agent on healthy tasks (set 4; 12-46% across sets) |
| 0 false passes | in a 1,120-trial audit, with every seeded defect caught (210/210) |
| Within 5% | of hand-written Playwright's speed on healthy flows (median 1.05x): the checks cost almost nothing |
npx -y @vedhajs/mcp --allow-domain localhost # MCP server for your agent
npm install -D @vedhajs/cli # or the CLI / SDK for checked steps
Agent-side demo (Playwright PASS vs Vedha FAILED on the same silent 500): npx tsx examples/demo/run.ts
Same model, same tasks, written before any run on them. Playwright MCP reports success whenever the page looks right; Vedha also checks what the server did.
| Held-out set 4 (pilot 8) | Playwright MCP | Vedha MCP |
|---|---|---|
| Broken features caught (seeded defects) | 10 / 18 | 16 / 18 |
| Healthy tasks completed | 18 / 18 | 17 / 18 |
| Median tokens, healthy tasks | 76,018 | 52,990 (-30%) |
| Median tokens, defect tasks | 210,000 | 139,029 (-34%) |
| Median wall time, healthy tasks | 169 s | 176 s (+4%) |
The lead held on every held-out set: 10 vs 4 of 14 (set 2), 15 vs 10 of 18 (set 3) and 16 vs 10 of 18 (set 4), or 41 vs 24 caught overall.
Fine print: LLM results are from one free model (minimax/minimax-m3.1-flash) at 3 trials per cell. When an expert writes every check by hand, Vedha and Playwright tie; Vedha's lead is with typical checks and with AI agents. All numbers