MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
Descripción del creador (original en inglés)
Web-app QA for AI agents: 73 Playwright tools — auth, E2E, a11y/SEO/GEO, Web Vitals + Lighthouse
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
El creador no incluyó detalles de instalación en el registro. Consulta el README del creador más abajo.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub segentic-lab, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.10.4) llegó al registro hace 4 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code.
En la misma categoría: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
An MCP server retrieving transcripts of YouTube videos
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
An MCP server that gives AI agents 74 Playwright tools to QA, test, and analyze web apps — static sites, SPAs, and apps behind a login — returning hard verdicts, not screenshots to squint at. Not a thin wrapper around browser APIs; the tools are shaped around how agents actually work:
assert_condition returns passed: true/false with the actual value; checks return structured issues.auto_fill_form detects, infers, and fills a whole form; interact_and_test batches 25 action types with checks; test_project crawls and audits an entire site.Playwright + headless Chrome underneath; site crawling, responsive testing, and screenshot diffing on top. Works with any MCP client — Claude Code, Codex, Cursor, Windsurf, Gemini CLI, custom agents, or anything else that speaks MCP over stdio.
playwright-mcp is excellent at what it is: general browser control over MCP, with tools that mirror Playwright's own API. If the job is "browse this site, click around, extract something," use it.
Periscope exists for a different job: testing and auditing a site or web app, then reporting findings — and its tools encode the testing knowledge an agent would otherwise have to reinvent every session:
| Raw browser control | Periscope | |
|---|---|---|
| Verifying an outcome | Read a screenshot or DOM dump and judge | assert_condition → hard passed: true/false + actual value |
| Filling a form | One call per field, agent invents test data | auto_fill_form — detects fields, infers realistic data, reports per-field failures |
| Auth | Re-login by scripting clicks each session | Projects persist form/basic/cookie auth; sessions share the logged-in context |
| Site-wide audit | Loop pages manually | test_project — crawl + accessibility/SEO/GEO/visual/functionality checks + saved report |
| Diagnosing a broken page | Ask for logs, replay requests | Response bodies, console, and network are captured automatically; mock APIs with intercept_network |
| Silent failures | Drag "succeeds," nothing moved | Flagged in the result, with the recovery path spelled out |
| AI-readiness audits | — | robots.txt AI-crawler access, llms.txt, WebMCP annotations, JSON-LD, plus real Lighthouse scores |
The two aren't rivals — an agent can happily use playwright-mcp for browsing tasks and Periscope when it's wearing the QA hat. Periscope's design bets are simply about that hat: fewer, higher-level calls; structured verdicts instead of raw page state; and errors written to tell the agent what to do next.
MCP client (AI agent) --> MCP Server (stdio) --> Playwright (Headless Chrome)
| | +-- Projects (JSON) +-- Persistent Sessions +-- Screenshots (PNG) +-- Network Interception +-- Reports (JSON) +-- Device Emulation +-- Videos (WebM)
How it works: your MCP client connects to this server over stdio. The server exposes 74 tools the agent can call to create projects, configure authentication, crawl websites, run static checks, and interactively test web applications using persistent browser sessions. Results (JSON + screenshots + videos) are returned to the agent for analysis.
periscope-mcp/
├── server.py # MCP server entry point (stdio wiring + dispatch) ├── tool_schemas.py # All 74 MCP tool definitions (schemas) ├── runtime.py # Shared singletons (project store, sessions, browser) ├── coercion.py # Argument coercion for MCP clients with stale schemas ├── handlers/ # Tool handlers, grouped by category │ ├── registry.py # @tool(name) decorator + HANDLERS registry │ ├── projects.py # create/list/get/delete project │ ├── auth.py # form login, basic auth, cookies, copy_auth │ ├── static_testing.py # test_url, crawl, test_project, reports, responsive │ ├── session_tools.py # open/close/list sessions, viewport, history │ ├── interactive.py # click, fill, steps, element queries, dialogs │ ├── analysis.py # forms, links, keyboard nav, tables, toasts, contrast │ ├── advanced.py # network mocking, storage, iframes, emulation, recording │ ├── agent_speed.py # assertions, smart find, auto-fill, snapshots │ ├── web.py # web_search, web_fetch │ ├── discovery.py # describe_tools catalog │ └── system.py # periscope_system: status, self-update, agents_md ├── tester.py # Playwright browser control + test orchestration ├── crawler.py # Page discovery (BFS crawl, same-domain only) ├── projects.py # Project CRUD + auth config storage ├── auth.py # Authentication handlers (form, basic, cookies) ├── sessions.py # SessionManager + PageSession — persistent page lifecycle ├── interactions.py # Interaction primitives (click, fill, execute_steps) ├── utils.py # Screenshot comparison (Pillow pixel diff) ├── config.py # Global settings (timeouts, paths, session limits) ├── checks/ │ ├── visual.py # Broken images, favicon, overflow, small text │ ├── accessibility.py # Alt text, labels, headings, lang, ARIA, keyboard nav │ ├── functionality.py # Broken links, forms, SEO, performance, link checker │ └── geo.py # GEO/agentic search: robots.txt AI crawlers, llms.txt, WebMCP, JSON-LD ├── tests/ # Unit tests (no browser) + tests/e2e/ (real browser + fixture pages) ├── data/ # Created at runtime (gitignored — contains credentials) ├── Dockerfile ├── docker-compose.yml └── .mcp.json.example # MCP registration template (copy to .mcp.json)
One command — clone and install:
git clone https://github.com/segentic-lab/periscope-mcp.git && cd periscope-mcp && ./install.sh
Fully unattended (no confirmation prompts):
git clone https://github.com/segentic-lab/periscope-mcp.git && cd periscope-mcp && ./install.sh -y
Already cloned? Just run ./install.sh from the repo directory.
The script installs apt prerequisites, creates the venv, installs Python dependencies and Playwright's Chromium, runs a headless self-test, and generates mcp-config.json with the correct absolute paths for this install (copy or merge it into your project's .mcp.json). Useful flags:
./install.sh --system-chromium — use an existing Chromium/Chrome (sets CHROMIUM_PATH) instead of downloading Playwright's build./install.sh --skip-deps — never touch apt / use sudo./install.sh -y — non-interactive (no confirmation prompts)On any other platform the script doesn't modify your system — it prints the exact commands to run for your OS (./install.sh --manual macos|fedora|arch|suse|windows to pick explicitly).
./update.sh
Pulls the latest source from GitHub (git pull --ff-only) and refreshes the install: Python dependencies, Playwright browser (kept on system Chromium if that's what the install uses), the registry + headless-launch self-test, and a regenerated mcp-config.json. Works on any platform with an existing install. Your data/ directory (projects, credentials, screenshots, reports) is never touched.
./update.sh --force — stash local modifications to tracked files first (recover with git stash pop)./update.sh --full — also re-check apt prerequisites on Debian/Ubuntu (uses sudo)If you have local modifications, the script refuses and lists them instead of overwriting.
# Clone the repo
cd periscope-mcp
python3 -m venv venv source venv/bin/activate
pip install -r requirements.txt
playwright install chromium
docker compose up -d
See Docker Deployment section below.
Periscope is a standard stdio MCP server: point any MCP client at venv/bin/python server.py and you're done. ./install.sh generates mcp-config.json with the correct absolute paths for your machine; most clients accept that shape directly:
{
"mcpServers": { "periscope": { "command": "/path/to/periscope-mcp/venv/bin/python", "args": ["/path/to/periscope-mcp/server.py"] } } }
Client-specific examples:
.mcp.json (cp .mcp.json.example .mcp.json and adjust paths), or run claude mcp add periscope -- /path/to/venv/bin/python /path/to/server.py~/.cursor/mcp.json / ~/.codeium/windsurf/mcp_config.json~/.codex/config.toml: [mcp_servers.periscope] with command and args as aboveAfter configuring, restart your client.
Two options, depending on your agent:
SKILL.md (repo root; also exposed at skills/periscope/ in the Claude Code skill layout) is a Claude Code skill — it auto-triggers on web-testing tasks and loads a distilled operating guide (workflow decision table + the pitfalls) only when needed, costing ~0 context otherwise: ln -s "$(pwd)/skills/periscope" ~/.claude/skills/periscope
A symlink keeps it current with ./update.sh (copy the folder instead if you prefer a frozen version).
AGENTS.md contains a ready-made system-prompt block — workflows, tool-selection guidance, and known pitfalls. Paste its contents into your agent's system prompt (or custom instructions).Either way, the agent can always fetch the current full guide from the running server via periscope_system(action="agents_md") and the complete catalog via describe_tools().
| Tool | Description | Required Params |
|---|---|---|
create_project | Create a new testing project | name, base_url |
list_projects | List all projects | _(none)_ |
get_project | Get project details | name |
delete_project | Delete project + data | name |
| Tool | Description | Required Params |
|---|
| set_form_login | Configure
Solo se muestra el principio; lee el resto en el repo