mcp.film directory

Name: mcp.film directory
Author: c47-inc

作者：c47-inc版本 1.6.0i★ –i电脑或云端

注册名称film.mcp/directory

安装

概览

作者提供的说明（英文原文）

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i3 项中通过 3 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 电脑或云端
传输方式: stdio, Streamable HTTP
分类: 游戏、体育、美食与学习

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：3 项中通过 3 项
在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
可以做 16 件事（工具） search_film_mcps, get_film_mcp, list_film_categories, list_film_capabilities …
注册中心没有列出需要填写的密钥。

你需要

在云端运行无需安装任何东西，通过互联网连接
npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

适用于网页版、桌面版和手机版 Claude：

打开 Settings → Connectors
点击 Add custom connector
粘贴下面的地址，然后点击 Add
如果要求登录，请登录作者的服务

https://mcp.film/mcp

或者在你的电脑上运行

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

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

在终端中运行这条命令

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

或者在你的电脑上运行

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

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

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

或者在你的电脑上运行

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

或者在你的电脑上运行

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

ChatGPT 只能连接互联网上的服务器：

打开网页版 ChatGPT → Settings → Connectors (Apps)
点击 Create 或 Add（部分套餐需要先在 Advanced 中开启 Developer mode）
粘贴下面的地址，起个名字，然后保存
如果要求登录，请登录作者的服务

https://mcp.film/mcp

菜单名称可能因套餐和版本不同而略有差异。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

工具名称和描述与服务器在 2026年11月7日报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

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

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过域名 mcp.film 验证（注册中心会核实域名所有权）
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
✓ 通过
端点有响应
在 66 毫秒内响应（HTTP 200）
– 不适用
使用 OAuth 登录
无需登录
✓ 通过
工具描述中没有隐藏指令
已读取 16 个工具描述，未发现针对 AI 的隐藏指令

与“游戏、体育、美食与学习”中的其他服务器比较i

星标数多于此分类中其他 1283 个服务器的 0%
发布到注册中心的时间比此分类中其他 1283 个服务器的 75% 更新
基础检查结果等于或优于此分类中其他 1283 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

mcp.film directory 安全吗？

它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

两种方式都可以：安装在你的电脑上，或者连接云端版本。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.6.0）于11天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。

有什么可以替代它？

同一分类中有：slopped.site, Artsonia MCP, Canvas Parent MCP

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

slopped.site

作者：slopped.site

Read-only tools over slopped.site: AI-made art, lore, games, sites, the curriculum.

在云端运行今天更新

安装

Artsonia MCP

作者：chrischall

Parent/fan access to Artsonia student-art portfolios, comments, and fans (AI-built).

在你的电脑上运行今天更新

安装

Canvas Parent MCP

作者：chrischall

Canvas LMS (Instructure) for Claude — courses, grades, assignments, planner, files

在你的电脑上运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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

Tool	What it does
`recommend_film_mcps`	Match 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_mcps`	Tokenised, 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_mcp`	Full entry for one server: install, auth, tools, caveats, status, evidence level and last endpoint/package check
`list_film_categories`	All categories with pipeline stages and agent hints
`list_film_capabilities`	Capability tags (aliases folded in) ranked by server count; `min_count` 2 by default; paged
`get_film_capability`	Ranked server cluster for one tag; aliases resolve (`lipsync` → `lip-sync`, says `resolved_from`); paged
`get_install_config`	Copy-paste config or hosted remote URL for a client from `list_client_profiles`; a bad client returns `valid_clients`
`list_client_profiles`	Supported MCP client setup profiles and install-shape hints
`get_martini_setup`	Executable 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_pulse`	Operating pulse: freshness, verification queue, curator agenda, machine surfaces. `demand_signals` and `martini_growth_checks` are curator/ops sections served only by name
`list_film_playbooks`	Curated production stack recipes for common AI filmmaking jobs; paged
`get_film_playbook`	Full 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_recommendations`	Intent-routed MCP shortlists for common filmmaking jobs; paged
`get_film_recommendation`	Full recommendation with roles, reasons, fallbacks, and Martini handoff guidance
`plan_film_stack`	Recommended servers for every pipeline stage, biased by your brief (capabilities, taglines, descriptions)
`submit_listing`	Propose 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.

mcp.film directory

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

或者在你的电脑上运行

或者在你的电脑上运行

或者在你的电脑上运行

或者在你的电脑上运行

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“游戏、体育、美食与学习”中的其他服务器比较i

常见问题

替代选择

slopped.site

Artsonia MCP

Canvas Parent MCP

作者的 README

mcp-film

Tools

Response shapes

How it works