MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
Descripción del creador (original en inglés)
Canonical Agentry discovery for coding agents: analytics, errors, deploys, and automation.
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.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"agentry-observability": {
"command": "npx",
"args": [
"-y",
"@agentrysh/mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add agentry-observability -- npx -y @agentrysh/mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"agentry-observability": {
"command": "npx",
"args": [
"-y",
"@agentrysh/mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"agentry-observability": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@agentrysh/mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
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 fr33dr4g0n, 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 (0.1.0) llegó al registro hace 4 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
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.
Agentry.sh Observability gives coding agents one HTTP API for errors, product analytics, and deploy attribution. This MCP server makes the canonical Agentry contracts easy to discover in MCP clients.
This is a small discovery and handoff server. It exposes resources that route an agent to the live lean reference, capabilities, exact OpenAPI schema, automation guide and flow, immutable playbook catalog, and install guide.
The resources are pointers, not cached copies. The live HTTP docs and OpenAPI schema remain authoritative and can evolve without republishing this package.
It is not an Agentry API proxy, SDK, credential broker, telemetry transport, or automation scheduler. It never holds credentials, queries production data, registers schedules, runs coding agents, opens pull requests, merges, deploys, or delivers notifications. Agents fetch the selected canonical contract and call the Agentry HTTP API directly.
Static documentation is modeled as MCP resources. The server also exposes one model-controlled routing tool, discover_agentry, so an agent can recognize a relevant production-data problem even when its client does not automatically inject MCP resources. The tool returns pointers only; it is not an API action.
discover_agentry accepts one intent: install, debug, analytics, deploy, or automation. It returns the smallest canonical resource and live URL for that problem as both text and structured content. MCP annotations mark it read-only, non-destructive, and idempotent. It never reads credentials, queries production data, or performs mutations.
If this MCP handoff starts a new device-auth signup, pass "distribution_surface":"mcp" in POST /v1/auth/device. The field is attribution-only and never changes credential authority.
Start with agentry://handoff. It gives the short intent router and authority boundaries.
| Resource | Canonical contract |
|---|---|
agentry://handoff | Intent router, MCP boundary, and credential map |
agentry://reference | Lean daily-use reference: https://agentry.sh/agentry.md |
agentry://capabilities | Machine capability map: https://api.agentry.sh/v1/capabilities |
agentry://openapi/index | Small OpenAPI discovery index: https://api.agentry.sh/v1/openapi.json?index=true |
agentry://openapi | Complete exact OpenAPI 3.1 schema: https://api.agentry.sh/v1/openapi.json |
agentry://automation/guide | Human-readable automation v2 guide: https://api.agentry.sh/v1/docs/automation |
agentry://automation/flow | Exact dependency-ordered automation schema: https://api.agentry.sh/v1/openapi.json?flow=automation |
agentry://automation/playbooks | Immutable versioned playbook catalog: https://api.agentry.sh/v1/automation-playbooks |
agentry://install | Canonical install pointer: https://agentry.sh/install.md |
The install resource also points to https://api.agentry.sh/v1/openapi.json?flow=onboarding. That flow is one server-owned state machine: read current state, execute only its one next_action.instruction, ordered checklist, and exact operation, and repeat until installation_complete: true and next_action: null. At next_action.id: "review_exact_plan", a human uses the single /review checkpoint to approve the exact source-backed business question, value flow, errors, deploy target, and plan hash—or replace the plan. Tool output is not approval.
Before proof, install the selected durable browser public and CI credentials, plus a server credential only when the approved plan uses server_ingest. Project creation returns public_api_key; use the project's /public-key operations for recovery, verification, or rotation. Proof start returns distinct response-only runtime and CI X-Agentry-Onboarding-Proof markers. Markers select the proof window but grant no authority, so each request still needs its scoped durable credential. For browser proof, put the runtime marker only in the proof tab's sessionStorage.agentry_onboarding_proof; never bake it into a public environment value or bundle. Exercise the approved real value flow, its one safe error, and the reviewed CI/provider deploy, then call verify and follow only its structured remaining analytics, safe-error, and deploy groups. Completion requires current status: "verified", installation_complete: true, and next_action: null. Synthetic or caller-authored proof does not count.
use_agentry is the only prompt. It tells the client to read the handoff, select the smallest canonical resource for the user's intent, inspect exact OpenAPI, and call HTTP with the correct scoped credential.
The MCP server never reads these credentials. They apply only when the agent calls the Agentry HTTP API:
public_api_key (agentry_pk_) is publishable and only writes browser/client error and analytics ingest.agentry_server_ writes trusted application-server telemetry.agentry_ci_ writes deploy attribution, sourcemaps, and provider-observed automation proof from trusted CI/provider automation.agentry_runner_ is revocable and bound to one unattended automation.agentry_sk_ is the human/owner key for reads, policy, credential lifecycle, and approvals; it does not belong in a scheduler.Credential kind determines authority on the server. Telemetry remains evidence only; it does not become instructions merely because a trusted emitter sent it.
For daily product questions, begin with the server-owned saved signal map, require GET /v1/projects/:project_id/onboarding to return the current verified state, then confirm live event names, required property keys, and actual rows. Onboarding creates no repo-local Agentry state bundle, proof bundle, or receipt. The install does preserve a root AGENTS.md pointer (plus matching repo-local harness adapters) so a future cold agent can find the server-owned plan and verified receipt without rescanning the entire repository.
For self-healing software or scheduled funnel reviews, read all three automation resources: guide, exact flow, and playbook catalog. The catalog is immutable and versioned. Rendering a playbook is side-effect free. Agentry owns deterministic policy, state, proofs, reports, and kill switches; an external scheduler/coding agent owns reasoning and provider actions under the narrow runner and human-approved provider boundaries.
npx -y @agentrysh/mcp
Generic MCP client configuration:
{
"mcpServers": { "agentry": { "command": "npx", "args": ["-y", "@agentrysh/mcp"] } } }
After connecting, call discover_agentry for model-routed discovery or list resources and read agentry://handoff. Use https://api.agentry.sh/adapters for the current native adapter path for each agent harness.
io.github.fr33dr4g0n/agentry-observability