# AI Video Atlas

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

- name: com.aivideoatlas/atlas
- version: 1.0.0
- connection class: R0 (autonomous)
- trust: 100/100
- quality: 88/100 (good)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/com.aivideoatlas%2Fatlas
- tools: https://api.protogrid.dev/v1/servers/com.aivideoatlas%2Fatlas/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

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


## Trust

- hygiene: 100
- liveness: 99
- freshness: 100
- provenance: 100
- drivers: +repository +namespace-matches-repo +dns-namespace +website +reachable +uptime-97% +updated-9d-ago
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 88/100 (good)

### protocol: 75 (weight 25)

- warn `protocol.modern`: newest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
- n/a `protocol.stateless`: only applies to 2026-07-28 servers
- pass `protocol.transport`: streamable HTTP
- n/a `protocol.list_ttl`: only applies to 2026-07-28 servers

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 93 (weight 30)

- pass `tools.descriptions`: every tool has a description
- warn `tools.description_length`: 1 tools have descriptions over 1,024 characters, which crowds the context
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 10 of 10 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- pass `tools.token_cost`: about 3,724 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 100 (weight 15)

- pass `stability.changes`: 5 tool changes in 30 days
- pass `stability.rug_pull`: no tool changed its meaning under the same name

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 10, about 3,724 tokens to load them all
- tool set hash: 2cc10211c120514a546c4a2ff91d7bb3
- tools last changed: 2026-10-12T19:19:10.403Z
- badge: [![protogrid quality](https://protogrid.dev/badge/com.aivideoatlas/atlas.svg)](https://protogrid.dev/servers/com.aivideoatlas/atlas)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

- 2026-10-12 `find_shot` changed (description)
  - before: Search AI Video Atlas's library of 107 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).
  - after: 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).
- 2026-10-12 `fix_prompt` added
  - 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.
- 2026-10-12 `refine_shot` added
  - 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.
- 2026-10-12 `plan_shots` changed (description)
  - before: 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 count is honored; clips longer than an endpoint's documented max are split. The description is split into beats (sentences, clauses, and cues like "then", "cut to", "close-up", "wide"). For each beat it returns a shot type; a recommended model endpoint with its current vendor-stated $/s and source; the generation mode to use (t2v, i2v, reference-to-video, start/end frame, v2v, lip sync; only modes the vendor docs confirm) with a one-line reason; image prompts for any input frames, plus a shared character/product reference sheet when shots must match; an adapted prompt built from the best-matching sourced example (credited and linked), an estimated cost (5 s per shot by default), and a how-to workflow when one applies (lip sync, consistent character…). It also returns a total. budget_usd (total) and/or max_per_shot steer it to cheaper supported endpoints; each shot also offers a cheaper alternative with its cost, and the plan says if the budget can't be met. Use this for "plan / storyboard / make me a video of…" requests; use find_shot for a single shot. Before presenting, adapt the wording to the user's voice, context and brand (names, tone, product details), but keep the prices, models, modes, clip lengths and reference tags exactly as returned.
  - after: 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 count is honored; clips longer than an endpoint's documented max are split. The description is split into beats (sentences, clauses, and cues like "then", "cut to", "close-up", "wide"). For each beat it returns a shot type; a recommended model endpoint with its current vendor-stated $/s and source; the generation mode to use (t2v, i2v, reference-to-video, start/end frame, v2v, lip sync; only modes the vendor docs confirm) with a one-line reason; image prompts for any input frames, plus a shared character/product reference sheet when shots must match; an adapted prompt built from the best-matching sourced example (credited and linked), an estimated cost (5 s per shot by default), and a how-to workflow when one applies (lip sync, consistent character…). It also returns a total. budget_usd (total) and/or max_per_shot steer it to cheaper supported endpoints; each shot also offers a cheaper alternative with its cost, and the plan says if the budget can't be met. Use this for "plan / storyboard / make me a video of…" requests; use find_shot for a single shot. Before presenting, adapt the wording to the user's voice, context and brand (names, tone, product details), but keep the prices, models, modes, clip lengths and reference tags exactly as returned.
- 2026-10-12 `plan_shots` changed (description, inputSchema)
  - before: 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 count is honored; clips longer than an endpoint's documented max are split. The description is split into beats (sentences, clauses, and cues like "then", "cut to", "close-up", "wide"). For each beat it returns a shot type; a recommended model endpoint with its current vendor-stated $/s and source; the generation mode to use (t2v, i2v, reference-to-video, start/end frame, v2v, lip sync; only modes the vendor docs confirm) with a one-line reason; image prompts for any input frames, plus a shared character/product reference sheet when shots must match; an adapted prompt built from the best-matching sourced example (credited and linked), an estimated cost (5 s per shot by default), and a how-to workflow when one applies (lip sync, consistent character…). It also returns a total. budget_usd (total) and/or max_per_shot steer it to cheaper supported endpoints; each shot also offers a cheaper alternative with its cost, and the plan says if the budget can't be met. Use this for "plan / storyboard / make me a video of…" requests; use find_shot for a single shot.
  - after: 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 count is honored; clips longer than an endpoint's documented max are split. The description is split into beats (sentences, clauses, and cues like "then", "cut to", "close-up", "wide"). For each beat it returns a shot type; a recommended model endpoint with its current vendor-stated $/s and source; the generation mode to use (t2v, i2v, reference-to-video, start/end frame, v2v, lip sync; only modes the vendor docs confirm) with a one-line reason; image prompts for any input frames, plus a shared character/product reference sheet when shots must match; an adapted prompt built from the best-matching sourced example (credited and linked), an estimated cost (5 s per shot by default), and a how-to workflow when one applies (lip sync, consistent character…). It also returns a total. budget_usd (total) and/or max_per_shot steer it to cheaper supported endpoints; each shot also offers a cheaper alternative with its cost, and the plan says if the budget can't be met. Use this for "plan / storyboard / make me a video of…" requests; use find_shot for a single shot. Before presenting, adapt the wording to the user's voice, context and brand (names, tone, product details), but keep the prices, models, modes, clip lengths and reference tags exactly as returned.

Full history: https://api.protogrid.dev/v1/servers/com.aivideoatlas%2Fatlas/changes

## Remotes

- https://mcp.aivideoatlas.com/mcp (streamable-http, auth none, reachable true, uptime30d 0.972973)

## Packages

_none_

## Tools (10)

- `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.
- `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).
- `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.
- `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.
- `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.
- `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.
- `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 count is honored; clips longer than an endpoint's documented max are split. The description is split into beats (sentences, clauses, and cues like "then", "cut to", "close-up", "wide"). For each beat it returns a shot type; a recommended model endpoint with its current vendor-stated $/s and source; the generation mode to use (t2v, i2v, reference-to-video, start/end frame, v2v, lip sync; only modes the vendor docs confirm) with a one-line reason; image prompts for any input frames, plus a shared character/product reference sheet when shots must match; an adapted prompt built from the best-matching sourced example (credited and linked), an estimated cost (5 s per shot by default), and a how-to workflow when one applies (lip sync, consistent character…). It also returns a total. budget_usd (total) and/or max_per_shot steer it to cheaper supported endpoints; each shot also offers a cheaper alternative with its cost, and the plan says if the budget can't be met. Use this for "plan / storyboard / make me a video of…" requests; use find_shot for a single shot. Before presenting, adapt the wording to the user's voice, context and brand (names, tone, product details), but keep the prices, models, modes, clip lengths and reference tags exactly as returned.
- `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.
- `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.
- `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.