AI Video Atlas

com.aivideoatlas由 aivideoatlas.com 發布官方發布者v1.0.0更新於 Nov 1, 2026

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

已驗證Streamable HTTP可網頁執行媒體與設計AI 與機器學習

概覽

AI 產生的概覽

唯讀 MCP 伺服器,協助助理規劃 AI 影片鏡頭:有出處的提示詞、模型建議、提示詞指南與廠商公布的每秒價格。

可以做什麼
  • 依一段簡單描述規劃多鏡頭影片,拆成節拍,並為每個節拍提供有出處的鏡頭、模型、提示詞與預估成本
  • 搜尋有出處的 AI 影片鏡頭庫,取出符合鏡頭的完整提示詞,附創作者署名與來源
  • 依證據推薦影片模型,並查詢廠商公布的每秒價格及其來源與查核日期
  • 依用途取得操作流程,以及各模型的提示詞指引
  • 診斷生成結果不理想的鏡頭,並給出具體的提示詞修改建議
  • 查看最近 N 天新增的鏡頭與指南
適用情境
適合在撰寫或調整 AI 影片生成提示詞、在多個影片模型間做選擇、估算片段成本,或尋找其他創作者公開分享的提示詞時使用。它是規劃與參考工具,本身不生成影片。
執行需求
需要遠端 streamable HTTP 端點;未宣告任何驗證、帳號、金鑰或環境變數。任何支援遠端伺服器的 MCP 用戶端皆可使用;部分用戶端需開啟開發人員模式或付費方案才能新增自訂連接器。公開執行個體約限制為每用戶端每分鐘 60 次請求。
安裝前請注意
唯讀且免金鑰,伺服器端不進行生成。查詢會以匿名方式記錄(工具名稱、截斷後的查詢文字、參數名稱、結果數量、延遲、用戶端名稱),並公開每日彙總計數,因此不要在查詢中填入個人資訊。結果帶有創作者與廠商署名,展示時應予保留。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 AI Video Atlas,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/com.aivideoatlas%2Fatlas/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "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]

來源: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

輸入參數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.

輸入參數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.

輸入參數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).

輸入參數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.

輸入參數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.

輸入參數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.

輸入參數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.

輸入參數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.

輸入參數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.

輸入參數dayskindlimit

版本歷史

1
  1. v1.0.0最新Oct 11, 2026

評分

已滿足適用檢查項的 100%

該評分衡量此收錄條目的完整程度,依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價,也不表示伺服器安全或目前可用:可連通性是帶日期的檢查結果,評分也不包含安裝量或使用者評價。

  • 近 30 天內可連通必需25 分
  • 已宣告認證方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 圖示10 分
  • 文件15 分
  • 授權條款10 分