MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
Beschreibung vom Entwickler (englisches Original)
Web-app QA for AI agents: 73 Playwright tools — auth, E2E, a11y/SEO/GEO, Web Vitals + Lighthouse
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Der Entwickler hat in der Registry keine Installationsangaben gemacht. Sieh dir unten die README des Entwicklers an.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto segentic-lab bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.10.4) kam vor 4 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code.
In derselben Kategorie: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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
Nur der Anfang wird gezeigt; den Rest findest du im Repo