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PopNx MCPServidores MCP para quienes usan IA · explicados con palabras sencillas

mcp.film directory

de c47-incversión 1.6.0i★ –iComputadora o nube
Nombre en el registrofilm.mcp/directory

Instalar

Resumen

Descripción del creador (original en inglés)

Search the curated directory of MCP servers for AI filmmaking and plan a full production stack.

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Computadora o nube
  • Transporte: stdio, Streamable HTTP
  • Categoría: Búsqueda y memoria de IA

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Funciona en Claude para web, escritorio y móvil:

  1. Abre Settings → Connectors
  2. Presiona Add custom connector
  3. Pega la dirección de abajo y presiona Add
  4. Si te pide iniciar sesión, inicia sesión en el servicio del creador
SettingsConnectors1+ Add2https://…3
https://mcp.film/mcp

O ejecútalo en tu computadora

Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json

{
  "mcpServers": {
    "directory": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-film"
      ]
    }
  }
}

Ejecuta esto en tu terminal

claude mcp add --transport http directory https://mcp.film/mcp

O ejecútalo en tu computadora

claude mcp add directory -- npx -y mcp-film

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

{
  "mcpServers": {
    "directory": {
      "url": "https://mcp.film/mcp"
    }
  }
}

O ejecútalo en tu computadora

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

{
  "mcpServers": {
    "directory": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-film"
      ]
    }
  }
}

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.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

{
  "servers": {
    "directory": {
      "type": "http",
      "url": "https://mcp.film/mcp"
    }
  }
}

O ejecútalo en tu computadora

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

{
  "servers": {
    "directory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "mcp-film"
      ]
    }
  }
}

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.

ChatGPT solo se conecta a servidores en internet:

  1. Abre ChatGPT en la web → Settings → Connectors (Apps)
  2. Presiona Create o Add (en algunos planes primero hay que activar Developer mode en Advanced)
  3. Pega la dirección de abajo, ponle un nombre y guarda
  4. Si te pide iniciar sesión, inicia sesión en el servicio del creador
SettingsConnectors1+ Create2https://…3
https://mcp.film/mcp

Los nombres de los menús pueden variar un poco según el plan y con el tiempo.

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.

Qué puede hacer

Nombres y descripciones de las herramientas tal como los informó el servidor el 7 nov 2026. Los escribió el creador, no nosotros; los mostramos como texto sin formato.

  • search_film_mcps

    Search the mcp.film directory of MCP servers for AI filmmaking. Filter by free-text query (matches name, vendor, tagline, description, capabilities, tools, category, pricing, auth, and the creative apps a server drives by name or alias, e.g. 'c4d' or 'resolve'), category id, or c

  • get_film_mcp

    Get the full mcp.film entry for one server by slug: description, install commands, auth requirements, sample tools, links, field notes (caveats), and machine-checked evidence (evidence level, last_probe of the hosted endpoint, package_check).

  • list_film_categories

    List all mcp.film categories with ids, pipeline stage, and per-category agent hints.

  • list_film_capabilities

    List capability tags in the mcp.film registry, ranked by number of matching servers (tag, page url, counts). Paged. Use get_film_capability for the server cluster behind one tag.

  • get_film_capability

    Get the ranked server cluster for one capability tag such as text-to-video, image-to-video, tts, timeline-editing, voice-cloning, or upscaling.

  • get_install_config

    Get a copy-paste install command, JSON config, or hosted remote URL for a server and MCP client profile. Includes required env vars and auth type.

  • list_client_profiles

    List the MCP client setup profiles mcp.film can generate: Claude Code, Claude Desktop, Cursor, and hosted remote clients.

  • submit_listing

    Propose a new MCP server for the mcp.film directory. Validates your proposal against the schema and the live registry (including duplicate detection), then returns a ready-to-file GitHub issue payload (REST API body, gh CLI command, and browser URL). Submissions are claims, never

  • get_catalog_pulse

    Get the mcp.film operating pulse: catalog freshness, verification queue, curator agenda, and machine-readable surfaces. Use this before deciding what to update next. The curator/ops sections demand_signals and martini_growth_checks are opt-in by section and not part of 'all'.

  • list_film_playbooks

    List mcp.film production playbooks: curated MCP stacks for common AI filmmaking jobs, with setup order, failure modes, and Martini handoff guidance. Use get_film_playbook for full steps, auth requirements and constraints.

  • get_film_playbook

    Get one production playbook by id, including primary servers, setup order, auth requirements, workflow steps, failure modes, fallback servers, constraints, and links to each server's mcp.film page. Pass client (claude_code, claude_desktop or cursor) to get only a copy-paste insta

  • list_film_recommendations

    List mcp.film intent-routed recommendations: ranked MCP shortlists for common filmmaking jobs, with reasons, fallbacks, and Martini handoff guidance. Use get_film_recommendation for full detail.

  • get_film_recommendation

    Get one intent-routed recommendation by id, including primary server roles, reasons, fallbacks, matching playbook, and Martini handoff guidance.

  • recommend_film_mcps

    Given a filmmaking brief, return the closest intent-routed MCP recommendations: one compact pick per primary server (slug, role, why, remote, official, verified) plus compact fallbacks. Use hosted_only=true when the agent cannot spawn local tools; get_film_mcp for full server det

  • plan_film_stack

    Get a recommended set of MCP servers covering the whole film pipeline (develop → visualize → shoot → sound → cut → finish → ship). Returns 1-3 picks per stage, ranked by official status and hosting. Pass a brief to bias picks (matched against capabilities, taglines and descriptio

  • get_martini_setup

    Ordered, executable runbook for connecting a user to Martini (the sponsor's full-stack AI film studio) with the fewest human steps: a resumable state model (needs_account → needs_authorization → needs_billing → needs_project → ready), the human steps, agent steps tailored to the

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con el dominio mcp.film (el registro comprueba quién es dueño del dominio)

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • ✓ Superada

    El endpoint responde

    Respondió en 66 ms (HTTP 200)

  • – No aplica

    Inicio de sesión con OAuth

    No hace falta iniciar sesión

  • ✓ Superada

    Sin instrucciones ocultas en las descripciones de herramientas

    Leímos 16 descripciones de herramientas y no encontramos instrucciones ocultas para la IA

Comparado con otros en Búsqueda y memoria de IAi

  • Más estrellas que el 0% de los otros 7275 servidores de esta categoría
  • Publicado en el registro más recientemente que el 78% de los otros 7275 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 7275 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro mcp.film directory?

Superó 3 de 3 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.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Funciona de las dos formas: instálalo en tu computadora o conéctate a la versión en la nube.

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (1.6.0) llegó al registro hace 10 días.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. Se conecta por URL, así que también funcionan las apps que aceptan servidores MCP remotos, como ChatGPT en modo desarrollador.

¿Qué puedo usar en su lugar?

En la misma categoría: Context7, Codebase Memory, Exa

Alternativas

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

Context7

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Codebase Memory

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Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

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Exa

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README del creador

Abrir el README

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.

mcp-film

The mcp.film directory as an MCP server. Give your agent a map of every MCP server worth knowing in AI filmmaking — searchable by capability, with copy-paste install configs and a full-pipeline planner.

claude mcp add mcp-film -- npx -y mcp-film

Or connect the hosted endpoint — same tools, no local install:

claude mcp add --transport http mcp-film https://mcp.film/mcp

Speaks MCP 2025-11-25, 2025-06-18 and 2025-03-26 (an unknown offered version gets the latest). initialize returns short instructions: start with recommend_film_mcps(brief).

Tools

ToolWhat it does
recommend_film_mcpsMatch a free-text brief to the best routes and first servers to connect. Hard constraints first (constraints object or phrases in the brief), then relevance; confidence says how well the brief matched
search_film_mcpsTokenised, alias-aware search (lipsync = lip-sync = lip sync; tts = text to speech; 4k = upscale; creative-app names and aliases such as c4d, resolve or fcp find the servers that drive that app); filter by category, capability, official, remote, answering_only (hosted endpoint answered the daily check); paged
get_film_mcpFull entry for one server: install, auth, tools, caveats, status, evidence level and last endpoint/package check
list_film_categoriesAll categories with pipeline stages and agent hints
list_film_capabilitiesCapability tags (aliases folded in) ranked by server count; min_count 2 by default; paged
get_film_capabilityRanked server cluster for one tag; aliases resolve (lipsync → lip-sync, says resolved_from); paged
get_install_configCopy-paste config or hosted remote URL for a client from list_client_profiles; a bad client returns valid_clients
list_client_profilesSupported MCP client setup profiles and install-shape hints
get_martini_setupExecutable runbook for connecting a user to Martini (the sponsor): resumable state model, human steps, agent steps per client / shell / browser, quote-first generation contract, structured cost, when not to
get_catalog_pulseOperating pulse: freshness, verification queue, curator agenda, machine surfaces. demand_signals and martini_growth_checks are curator/ops sections served only by name
list_film_playbooksCurated production stack recipes for common AI filmmaking jobs; paged
get_film_playbookFull playbook with primary servers, setup order, auth requirements, steps, constraints, fallbacks. Pass client (claude_code, claude_desktop, cursor) for only a copy-paste install of the whole stack
list_film_recommendationsIntent-routed MCP shortlists for common filmmaking jobs; paged
get_film_recommendationFull recommendation with roles, reasons, fallbacks, and Martini handoff guidance
plan_film_stackRecommended servers for every pipeline stage, biased by your brief (capabilities, taglines, descriptions)
submit_listingPropose a new server: validated, deduped against the live registry, returned as a ready-to-file GitHub issue payload (a mcp.film maintainer verifies against primary sources before listing)

Response shapes

Every result is compact JSON in content[0].text and the same object in structuredContent.

Lists (search_film_mcps, get_film_capability, list_film_capabilities, list_film_playbooks, list_film_recommendations) carry count, total, limit, offset, next_offset and truncated. Nothing is cut silently: pass offset: next_offset for the next page. Default limit is 20 (50 for capabilities), maximum 100.

Compact server (search results, capability clusters, stack picks): slug, url, verified, caveat, name, vendor, official, category, tagline, pricing, remote, evidence, capabilities plus status when the listing carries one. caveat is the entry's caveats[0], else the first sentence of its notes that is not a verification log line. get_film_mcp is always the full entry.

Recommendation pick (recommend_film_mcps): primary_servers[] and the alias primary[] hold {slug, name, role, why, remote, official, verified, status?, caveats?}; fallback_servers[] hold {slug, name, remote, official, verified, status?}. Pass verbosity: "full" for the complete server objects. Each route has match: {score, strength}; the reply has confidence (strong | fair | weak). When Martini is a primary pick or the brief asks for production state, the route carries martini_handoff (prose) and martini_handoff_structured (when, not_when, first_calls, install {mcp, cli}, signup_url, setup_tool, tracked_url, disclosure).

Constraints (recommend_film_mcps): constraints: {local_only, no_upload, hosted_only, has_shell, free_only, os}, also inferred from phrases such as "locally", "without uploading", "confidential", "offline", "no budget", "free tools", "no local install". Violating picks are removed before scoring and named in excluded_by_constraints; if nothing survives, the reply is no_match with unsatisfied_constraints and relevant_routes.

Whole-stack install (get_film_playbook with client): {id, title, url, install_script, note}. install_script is {client, servers, script} for claude_code (one commented claude mcp add line per server) or {client, servers, file, config, note} for claude_desktop / cursor (one merged mcpServers object), plus checklist[] (slug, name, auth, env_var?, where?: the keys, OAuth sign-ins and local apps the stack needs) and skipped[] (slug, name, reason, listing). Primary and workflow servers only, never fallbacks. Retired listings, listings with install.generate: false, and listings with no mechanical install for that client are skipped with the reason, never guessed at. It is the same block shown on mcp.film/playbooks/{id}/ and in its .md.

Evidence. Every server carries evidence, derived at build time from a daily anonymous check (no LLM, no credentials, tools are never called): endpoint_answered (the hosted endpoint completed an MCP initialize, or answered with a 401/403 sign-in challenge, within the last 7 days) > package_resolves (its npm/PyPI package exists, checked within 7 days) > docs_checked (only the curator's verified date). get_film_mcp adds last_probe: {date, result, protocol_version, tool_count, anonymous_tools_list, auth_required, oauth_metadata, consecutive_failures, last_ok} (result: ok | auth_required | handshake_only | error | unreachable) and package_check: {date, registry, name, found, latest_version, last_ok}, each null when not applicable. A failing check never changes status; a curator does that after re-verifying.

Status. Listings with status dead, archived or deprecated are hidden from search, recommendations and stack plans unless include_inactive: true.

Zero results. search_film_mcps returns did_you_mean (nearest capabilities, categories, servers) and a hint; an unknown category or capability is an error carrying valid_categories / did_you_mean.

How it works

Zero dependencies. Fetches the live registry from https://mcp.film/api/registry.min.json and production playbooks from https://mcp.film/api/playbooks.json, plus recommendation routes from https://mcp.film/api/recommendations.json and the operating pulse from https://mcp.film/api/pulse.json (5s timeout each), then falls back to bundled snapshots offline. Live data is cached for an hour and refreshed in the background (MCP_FILM_CACHE_TTL_MS); MCP_FILM_OFFLINE=1 serves the snapshots only and MCP_FILM_API_BASE points the fetch at another host. get_catalog_pulse reports data_source and observed_at. Data is curated and re-verified continuously at github.com/c47-inc/mcp-film.

MIT.

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