# AI Video Atlas (remote · mcp.aivideoatlas.com)

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

- Trust score: 72/100 (medium)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-10-23

## Components

- remote · `mcp.aivideoatlas.com`: 72/100 (this document), [markdown](https://verifymcp.io/servers/com-aivideoatlas-atlas/mcp.md), [page](https://verifymcp.io/servers/com-aivideoatlas-atlas/mcp)

## Channel facts

- Endpoint: `https://mcp.aivideoatlas.com/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.0.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-10-23.

- **Endpoint Security**: 63/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS enforcement could not be verified: the plaintext port answered with HTTP 405, which proves neither a plaintext path nor enforcement.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 82/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Tool/resource definitions use about 3762 tokens (~99/item across 38 items; 10 tools + 28 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 37/100
  - Stability observed for 11 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 89/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 66% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 10 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 12 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the AI Video Atlas MCP server?

AI Video Atlas is a hosted endpoint at https://mcp.aivideoatlas.com/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http com-aivideoatlas-atlas 'https://mcp.aivideoatlas.com/mcp'
```

### Cursor

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

### VS Code

```json
{
  "servers": {
    "com-aivideoatlas-atlas": {
      "type": "http",
      "url": "https://mcp.aivideoatlas.com/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.com-aivideoatlas-atlas]
url = "https://mcp.aivideoatlas.com/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "com-aivideoatlas-atlas": {
      "type": "remote",
      "url": "https://mcp.aivideoatlas.com/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add com-aivideoatlas-atlas --url 'https://mcp.aivideoatlas.com/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  com-aivideoatlas-atlas:
    url: "https://mcp.aivideoatlas.com/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "com-aivideoatlas-atlas": {
      "Transport": "http",
      "Url": "https://mcp.aivideoatlas.com/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add com-aivideoatlas-atlas -t streamable-http -u 'https://mcp.aivideoatlas.com/mcp'
```

### Other

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

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-10-22 (score 72, +1)

No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-20 (score 71, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-18 (score 70, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-16 (score 69, +1)

No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-13 (score 68, +1)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “find_shot” rewrote its description, which is the text the model reads
- [security] Tool “plan_shots” rewrote its description, which is the text the model reads
- [functional regression] Schema quality: 86 → 99
- [functional improvement] Tool coverage: 53% → 66%
- [functional improvement] Stability: unverified → 0.03
- [functional] New tool “fix_prompt”
- [functional] New tool “refine_shot”

### 2026-10-12 (score 67)

First indexed and scored.

## MCP tools (10)

### `plan_shots` (~940 tokens)

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…

Input parameters:

- `budget_usd` (number): Optional total budget in USD.
- `clip_seconds` (number): Seconds per shot (default 5).
- `description` (string, required): What the video should show, in plain words. Several beats are fine.
- `force_plan` (boolean): Return a shot plan even when the request reads as a pricing-only question
- `max_per_shot` (number): Optional maximum USD per shot.
- `mode` (string): Generation mode the user named. Kept for every shot unless the model cannot do it.
- `model` (string): Model the user named (e.g. "Seedance 2.5"). Atlas keeps it for every shot unless the vendor docs rule it out, and then says why. Always pass it when the user names one.
- `retries` (number): Optional retry allowance as a cost multiplier (e.g. 1.5 = budget 50% extra generations for rejected takes).
- `style` (string): Optional look applied to every shot, e.g. "35mm film, warm grade" or "anime".

### `refine_shot` (~302 tokens)

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.

Input parameters:

- `frame_notes` (string): Optional: what you see in the bad frames.
- `frame_url` (string): Optional link to a bad frame (Atlas does not open it; describe it in frame_notes).
- `issue` (string, required): What went wrong, in plain words.
- `mode` (string): Mode used: text-to-video, image-to-video, reference-to-video, start/end frame, video-to-video, lip sync.
- `model` (string): Model/endpoint used, e.g. "Seedance 2.5", "Veo 3.1 Fast".
- `prompt` (string, required): The exact prompt you used.
- `seconds` (number): Clip length used (helps check spoken-line fit).

### `fix_prompt` (~297 tokens)

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.

Input parameters:

- `frame_notes` (string): Optional: what you see in the bad frames.
- `frame_url` (string): Optional link to a bad frame (Atlas does not open it; describe it in frame_notes).
- `issue` (string, required): What went wrong, in plain words.
- `mode` (string): Mode used: text-to-video, image-to-video, reference-to-video, start/end frame, video-to-video, lip sync.
- `model` (string): Model/endpoint used, e.g. "Seedance 2.5", "Veo 3.1 Fast".
- `prompt` (string, required): The exact prompt you used.
- `seconds` (number): Clip length used (helps check spoken-line fit).

### `find_shot` (~278 tokens)

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).

Input parameters:

- `limit` (integer)
- `max_cost_usd` (number): Max estimated cost per clip in USD
- `model` (string): Only shots made with this model family (strict; comparisons that merely include it are excluded), e.g. kling, veo, seedance, hailuo, runway, luma, wan, ltx, sora
- `query` (string, required): What the shot should look like or achieve, in plain words.
- `shot_type` (string)

### `best_prompt` (~138 tokens)

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.

Input parameters:

- `model` (string): Target model family (optional).
- `shot` (string, required): A slug from find_shot or a plain description of the shot.

### `recommend_model` (~264 tokens)

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.

Input parameters:

- `budget_usd` (number): Total budget for all clips
- `clip_seconds` (number)
- `clips` (integer)
- `max_per_shot` (number): Max USD per clip
- `mode` (string): Required generation mode; detected from the shot text when omitted
- `shot` (string, required)

### `get_prices` (~113 tokens)

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.

Input parameters:

- `host` (string)
- `include_legacy` (boolean)
- `model` (string)
- `sort` (string)

### `get_workflow` (~182 tokens)

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.

Input parameters:

- `job` (string)
- `limit` (integer)
- `query` (string)
- `slug` (string)

### `get_guide` (~227 tokens)

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.

Input parameters:

- `kind` (string): Optional filter, e.g. camera or avoid
- `model` (string): Model family, e.g. kling, veo, seedance, runway, luma, hailuo, wan, ltx, grok-imagine
- `topic` (string)

### `whats_new` (~119 tokens)

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.

Input parameters:

- `days` (integer)
- `kind` (string)
- `limit` (integer)

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/com-aivideoatlas-atlas/mcp#diagnostics

## Score history

- 2026-10-23: 72
- 2026-10-22: 72
- 2026-10-21: 71
- 2026-10-20: 71
- 2026-10-19: 70
- 2026-10-18: 70
- 2026-10-17: 69
- 2026-10-16: 69
- 2026-10-15: 68
- 2026-10-14: 68
- 2026-10-13: 68
- 2026-10-12: 67

## Common questions

### What is the AI Video Atlas MCP server?

AI Video Atlas is an MCP server listed in the public MCP registry as com.aivideoatlas/atlas. Plan AI video shots: model picks, sourced prompts, prompting guides and real $/s costs. This page covers its hosted endpoint (https://mcp.aivideoatlas.com/mcp).

### Is the AI Video Atlas MCP server safe to use?

AI Video Atlas scores 72 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the AI Video Atlas MCP server expose?

AI Video Atlas exposes 10 tools: plan_shots, refine_shot, fix_prompt, find_shot, best_prompt, and 5 more. Their descriptions and schemas cost roughly 2,860 tokens of context every time the server is loaded.

### Does the AI Video Atlas MCP server require authentication?

No. We connected to AI Video Atlas without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the AI Video Atlas MCP server still maintained?

AI Video Atlas is still listed as active in the MCP registry. We last reached this channel on 23 October 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://mcp.aivideoatlas.com/mcp
- Repository: https://github.com/aivideoatlas/aivideoatlas-mcp
- Website: https://aivideoatlas.com/agents/
- Changelog RSS feed: https://verifymcp.io/servers/com-aivideoatlas-atlas/mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/com-aivideoatlas-atlas/mcp.json
- HTML version of this page: https://verifymcp.io/servers/com-aivideoatlas-atlas/mcp