AI Video Atlas

com.aivideoatlas由 aivideoatlas.com 发布官方发布者v1.0.0更新于 Nov 1, 2026

Plan AI video shots: model picks, sourced prompts, prompting guides and real $/s costs.

已验证Streamable HTTP可网页运行媒体与设计AI 与机器学习

概览

AI 生成的概览

只读 MCP 服务器,帮助助手规划 AI 视频镜头:带来源的提示词、模型推荐、提示词指南和厂商公布的每秒价格。

可以做什么
  • 根据一段简单描述规划多镜头视频,拆分为节拍,并为每个节拍给出带来源的镜头、模型、提示词和预估成本
  • 检索带来源的 AI 视频镜头库,取出匹配镜头的完整提示词,附创作者署名与出处
  • 依据证据推荐视频模型,并查询厂商公布的每秒价格及其来源和核对日期
  • 按用途获取操作流程,以及各模型的提示词指导
  • 诊断生成效果不佳的镜头,并给出具体的提示词修改建议
  • 查看最近 N 天新增的镜头和指南
适用场景
适合在撰写或调试 AI 视频生成提示词、在多个视频模型之间做选择、估算片段成本,或寻找其他创作者公开发布的提示词时使用。它是规划与参考工具,本身不生成视频。
运行要求
需要远程 streamable HTTP 端点;未声明任何认证、账号、密钥或环境变量。任何支持远程服务器的 MCP 客户端均可使用;部分客户端需开启开发者模式或付费套餐才能添加自定义连接器。公共实例约限制为每客户端每分钟 60 次请求。
安装前请注意
只读且无需密钥,服务器端不进行生成。查询会以匿名方式记录(工具名、截断后的查询文本、参数名、结果数量、延迟、客户端名称),并公开发布每日汇总计数,因此不要在查询中填写个人信息。结果带有创作者和厂商署名,展示时应予保留。

安装

在 SourceWeft 中

  1. 打开 控制台中的 AI Video Atlas,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/com.aivideoatlas%2Fatlas/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "ai-video-atlas": {
      "type": "http",
      "url": "https://mcp.aivideoatlas.com/mcp"
    }
  }
}

README

AI Video Atlas MCP server

Give your AI agent a video director. This is the open-source MCP server (and REST API) behind AI Video Atlas. It lets Claude, ChatGPT, Cursor, Gemini, Grok Bot or any MCP client plan AI video shots, pick models, find sourced prompts, and price a clip using vendor-stated $/second, all from Atlas data.

  • Live endpoint: https://mcp.aivideoatlas.com/mcp (streamable HTTP, no auth, free)
  • REST API: https://mcp.aivideoatlas.com/v1/<tool>. The OpenAPI spec is at https://mcp.aivideoatlas.com/openapi.json.
  • Agent guide: https://aivideoatlas.com/agents/

It's read-only and deterministic: no AI generation happens on the server, and every answer is built from Atlas's sourced data, with links back to the original creators and vendor pages.

Connect

Claude (web and desktop): open claude.ai → Settings → Connectors, click Add custom connector, name it AI Video Atlas and paste https://mcp.aivideoatlas.com/mcp. No sign-in is needed. On Team and Enterprise plans, an owner may need to add the connector first. Claude Code:

claude mcp add --transport http ai-video-atlas https://mcp.aivideoatlas.com/mcp

ChatGPT: custom MCP connectors need developer mode, a beta for paid plans (Pro, Business, Enterprise, Edu). It isn't available on Free. Open Settings → Apps & Connectors → Advanced settings and turn on Developer mode. Then click Create, name it AI Video Atlas, paste the URL and set authentication to No authentication. Building a custom GPT instead? Go to Configure → Actions → Import from URL and use https://mcp.aivideoatlas.com/openapi.json.

Cursor: use the one-click "Add to Cursor" button on https://aivideoatlas.com/agents/, or add this to ~/.cursor/mcp.json:

json
{"mcpServers":{"ai-video-atlas":{"url":"https://mcp.aivideoatlas.com/mcp"}}}

Gemini: Gemini CLI (free):

gemini mcp add --transport http ai-video-atlas https://mcp.aivideoatlas.com/mcp

In the Gemini app, custom connected apps are limited to Gemini Spark on personal Google accounts in the US (18+). If you have it, add a custom app with the URL above. Gems can't connect to outside tools.

Grok Bot: paste this into your Grok Bot chat:

Add this MCP server: name aivideoatlas, URL https://mcp.aivideoatlas.com/mcp (no key needed).

Grok Bot confirms before adding it, and the tools are available from your next message.

Any MCP client / API: use the URL above (streamable HTTP, no auth). For plain HTTP:

curl "https://mcp.aivideoatlas.com/v1/find_shot?query=drone+shot&limit=3"

VS Code, Windsurf and other MCP clients take the same URL.

Tools

ToolWhat it does
plan_shotsPlan a multi-shot video from a plain description: splits it into beats and picks a sourced shot, model, prompt and estimated cost for each. Deterministic, built only from Atlas data.
find_shotSearch the library of sourced AI video shots (real prompts creators posted, with the model, estimated cost and a link to the original).
best_promptGet the full sourced prompt that best matches a shot, optionally for a target model, with creator credit and source.
recommend_modelRecommend a model for a shot with evidence: how many sourced shots used each model, the cheapest vendor-stated $/s, and an estimated total.
get_pricesVendor-stated $/second prices with source URL and check date. Filter by family or host. Discontinued rows are excluded unless asked for.
get_workflowHow-to workflows, tools and model guides grouped by job (lip-sync, upscaling, character consistency and more).
get_guidePer-model prompting guidance (structure, camera, modes, consistency, audio, what to avoid, limits), sourced from vendor docs plus proven Atlas examples.
whats_newThe newest shots and guides added in the last N days.

The server also exposes MCP resources (for example, atlas://guides/<model>) and server instructions that tell agents when to use each tool.

Rate limits

The public instance is free and keyless. It allows about 60 requests a minute per client (per IPv4 address, or per IPv6 /48), enforced by a Durable Object. A backup edge rule also applies. Over the limit, you get HTTP 429 with retry-after.

Privacy

  • No accounts, keys or cookies.
  • IP addresses are never stored or logged. They're used only in memory to apply the rate limit.
  • Workers invocation logs are off. The only log line is an anonymous atlas_query event: tool name, query text (lowercased and truncated), argument names, result count, latency and client name, if the client sends one. These are used to see which tools and topics are useful.
  • Daily aggregate counts (sessions, tool calls by tool, MCP vs REST, client names) are kept in a Durable Object and published at https://mcp.aivideoatlas.com/stats/daily. Counts only: no IPs, no query text.
  • Don't put personal information in queries.
  • Full policy: https://aivideoatlas.com/privacy/

Run your own copy

npm installnpm run build            # downloads the public data export from https://aivideoatlas.com/agent-data.json and inlines itnpx wrangler dev         # localnpx wrangler deploy      # needs your own Cloudflare account

npm run build writes dist/worker.mjs. To build from a local export instead, run node scripts/build.mjs --data=path/to/agent-data.json, or place it at data/agent-data.json (gitignored).

In wrangler.toml, set account_id (or use CLOUDFLARE_ACCOUNT_ID) and, optionally, your own custom-domain route. The Durable Object binding LIMITER → RateLimiter (SQLite-backed, available on the free plan) is already declared. No secrets are required.

Data and licensing

The code is MIT-licensed (see LICENSE). The data is not in this repo: it's AI Video Atlas's public export, with credit to the original creators and vendor sources linked on every item. Please keep those credits when you show results.

Registry

server.json is the entry for the official MCP Registry (com.aivideoatlas/atlas).

Contact: [email protected]

来源:README.md,提交 c2f8b2d

工具

10

plan_shots

plan shots

Goal: a usable take on the first generation, so fewer paid generations per shot. First review and rewrite the beat list (see brief_review): Atlas parses briefs with rules, so fix non-shots, missing subjects, placeholder lines and generic coverage before writing prompts. If the user names a model or mode, pass it in model/mode (it is kept for every shot unless the docs rule it out). Returns a GUIDANCE PACKAGE, not final prompts: you (the agent) write each full video prompt yourself from it. Per shot: model + mode decision with reasons, the per-model/per-mode prompt structure and tips, 2-3 credited proven example prompts, capabilities/limits/prices with sources, techniques and director rules (practitioner hints at low weight), required assets with image-gen guidance, an 11-section quality checklist, and a starter draft (starting point only, never paste it as-is). If the user names a model or mode, pass it in model/mode (or keep it verbatim in description): Atlas keeps it for every shot unless the vendor docs rule it out, and explains why in user_named. Write each final prompt as flowing, sectioned prose sentences (subject/action, camera/lens/movement, location/light/color, style, audio), never field labels like speaker_id: or Subject:, and without stock filler phrases. Also returns intake (questions to ask the user + stated assumptions) and package_markdown (readable). Plan a multi-shot AI video from a plain description (deterministic, built only from Atlas data; no AI generation). Output is staged: decide (model/mode/clip length/aspect/cost with reasons and capability checks), direct (shot list with per-model prompts), assets (every reference/frame with status needs-generation|user-supplied|approved), image_prompts, and steps, which include a STOP: get the user to approve assets before generating any video. Global directives (same X in every shot, vertical, no text, style) apply to all shots; questions and request wording are never planned as shots; an explicit shot

输入参数modemodelstyleretriesbudget_usdforce_plandescription必填clip_secondsmax_per_shot

refine_shot

refine shot

Get to a usable take in as few further generations as possible when a shot came out wrong. Pass the prompt you used, the model, the mode and what went wrong in plain words ("feet sliding", "face drifted", "line cut off", "too glossy", "label morphing"), plus optional frame notes. Returns a diagnosis, specific prompt changes (from Atlas craft fixes and a per-model failure-mode library, credited), a revised prompt, and an escalation ladder: same-model fixes first (prompt, seed/variation, references, duration), then a mode change, then a model switch only as a last resort with the consistency cost stated. Adapt the revised prompt to the user's voice; no generation is run.

输入参数modeissue必填modelprompt必填secondsframe_urlframe_notes

fix_prompt

fix prompt

Alias of refine_shot. Fix a generated shot that came out wrong. Pass the prompt you used, the model, the mode and what went wrong in plain words ("feet sliding", "face drifted", "line cut off", "too glossy", "label morphing"), plus optional frame notes. Returns a diagnosis, specific prompt changes (from Atlas craft fixes and a per-model failure-mode library, credited), a revised prompt, and an escalation ladder: same-model fixes first (prompt, seed/variation, references, duration), then a mode change, then a model switch only as a last resort with the consistency cost stated. Adapt the revised prompt to the user's voice; no generation is run.

输入参数modeissue必填modelprompt必填secondsframe_urlframe_notes

find_shot

find shot

Search AI Video Atlas's library of 106 sourced AI video shots (real prompts that creators posted, with the model used, an estimated cost and a link to the original). Use this FIRST when the user describes a shot, scene, style or job ("product ad with a rotating can", "anime fight", "lip sync dialogue", "drone over mountains"). Filters: shot_type (ads, music, shorts, characters, product, cinematic, lipsync, anime), model (e.g. kling, veo, seedance), max_cost_usd (estimated cost of the clip; when the prompt states no length we use a 5-second clip). Returns prompt excerpts; call best_prompt with a slug for the full prompt. Do not use for prices alone (use get_prices) or for tools/workflows (use get_workflow).

输入参数limitmodelquery必填shot_typemax_cost_usd

best_prompt

best prompt

Get the full, sourced prompt that best matches a shot (free text or a slug from find_shot), optionally for a target model. Returns the exact prompt text as the creator posted it, the creator credit and source, the model it was made with, and factual adaptation notes when the target model differs (other Atlas shots on the target model and its current supported price). Atlas never claims how a prompt will perform on a model it was not made with; treat adaptation notes as guidance.

输入参数shot必填model

recommend_model

recommend model

Recommend which AI video model to use for a shot, with the evidence: how many sourced Atlas shots of that kind were made with each model, the cheapest supported vendor-stated $/s, and an estimated total for the clip length and number of clips. Pass budget_usd (total) and/or max_per_shot to drop models whose estimate exceeds it; over-budget models are listed with their cost. Each row includes the generation modes the vendor docs confirm (t2v, i2v, reference-to-video, start/end, v2v, lip sync; unknown where docs do not say). Legacy, deprecated and discontinued models are never recommended. Filters by the required mode FIRST (detected from the shot text or the mode argument; endpoints whose docs say unknown are excluded), then ranks the cheapest qualifying endpoint first and notes when a pricier one has more proof. Includes each endpoint's max clip length and splits long clips.

输入参数modeshot必填clipsbudget_usdclip_secondsmax_per_shot

get_prices

get prices

Vendor-stated AI video prices per second from AI Video Atlas's tracker, each with its source URL and the date we checked it. Filter by model family or host. Legacy, deprecated and discontinued rows are excluded unless include_legacy=true (then they are flagged by status). Use for "how much does X cost", "cheapest model", or before quoting any cost.

输入参数hostsortmodelinclude_legacy

get_workflow

get workflow

How-to workflows, tools and model guides for AI video, grouped by job: consistent-character (Keep a character consistent), lip-sync (Lip sync & talking heads), product-ad (Product ads & UGC), camera (Direct the camera), long (Longer & multi-shot videos), i2v (Animate a still image), edit (Edit or restyle footage), upscale (Upscale & smooth), local (Run it on your own GPU), models (Models & hosts). Pass job for a curated list, query for free text (e.g. "comfyui lipsync", "upscale to 4k"), or slug for one item in full. Returns sources, creators, license and price as stated.

输入参数jobsluglimitquery

get_guide

get guide

Prompting guidance from AI Video Atlas, sourced from vendor prompting docs (Google, BytePlus, Runway, Luma, MiniMax, Alibaba, LTX, xAI, fal) plus proven Atlas examples. Pass model (kling, veo, seedance, runway, luma, hailuo, wan, ltx, grok-imagine…) for prompt structure, camera language, what the model responds to, things to avoid and known limits; or topic (shot-vocabulary, tagging, consistent-characters, i2v-vs-r2v, setup-playbooks). Every item carries its source URL; items marked atlas-editorial are our summary, not a vendor claim. Call with no arguments to list what exists. Use before writing a prompt for a specific model.

输入参数kindmodeltopic

whats_new

whats new

What is new in AI Video Atlas: the newest sourced shots and guides added in the last N days (default 7), newest first, with X like counts where we have them ("new" otherwise). Use for "what's new in AI video", weekly roundups, or trend questions. For the full weekly digest (new models, price changes, new shots, legacy prices) read https://aivideoatlas.com/digest/latest.json.

输入参数dayskindlimit

版本历史

1
  1. v1.0.0最新Oct 11, 2026

评分

已满足适用检查项的 100%

该评分衡量此收录条目的完整程度,依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价,也不表示服务器安全或当前可用:可连通性是带日期的检查结果,评分也不包含安装量或用户评价。

  • 近 30 天内可连通必需25 分
  • 已声明认证方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分