lipdub-mcp
OfficialUse this MCP server to connect an AI client to LipDub and run/monitor LipDub 2 lip-sync renders.
Check that the API key works and identify the LipDub account.
Start a lip-sync render from a public video URL or YouTube link plus a public audio URL; this spends credits and requires user confirmation.
Check a render’s current status and retrieve its temporary download link.
Wait for a render to finish with bounded blocking instead of polling manually.
List recent renders to recover lost render IDs or review recent activity.
Use advanced inputs such as existing LipDub video/audio IDs, callback URL, and output filename.
Create renders is the only paid operation; checking, waiting, and listing renders are free.
The README also exposes a quick-dub prompt and quickstart/troubleshooting resources.
Allows YouTube video URLs to be used as the source video for LipDub renders, with YouTube links resolved automatically so a standard watch URL works as the video input.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@lipdub-mcpLip-sync https://example.com/talk.mp4 to the audio at https://example.com/audio.mp3"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
LipDub MCP server
Give your AI agent the ability to lip-sync video.
LipDub 2 makes a person in a video appear to speak a different audio track, with matched lip movement. This is an MCP server that exposes it to Claude, Cursor, Gemini CLI, Codex, VS Code, and any other MCP-compatible client.
LipDub 2 does not translate, transcribe, or generate speech. You bring the audio. If you want a video dubbed into another language, generate that audio first with a text-to-speech or voice-cloning tool, host it at a public URL, and pass that URL. It pairs naturally with a text-to-speech server for that first step.
What it looks like
You: Lip-sync https://example.com/keynote.mp4 to https://example.com/spanish.mp3
Claude: I'll use LipDub 2 for that. This will charge credits to your LipDub
account and can't be refunded — shall I go ahead?
You: yes
Claude: Started — render_id rnd_88213. A clip this length takes a few minutes; I'll wait.
...
Done. Here's your video: https://… (link expires, so grab it soon)Related MCP server: atsurae
Setup
1. Get an API key
Sign in at app.lipdub.ai.
Open Settings → API Keys: app.lipdub.ai/settings/api-keys
You must be an Owner or Admin on the account. Other roles are redirected to the dashboard with no explanation. If that link bounces you, ask an Owner or Admin on your team to generate the key for you.
Generate a key and copy it.
One key per user. LipDub issues a single API key per user, and generating a new one replaces the old one. If your account already uses its key for another integration, generating a fresh key will break it. Reuse the existing key, or use a separate Owner/Admin account for agent work.
2. Point your client at it
Nothing to install: npx fetches it on first run.
claude mcp add lipdub --env LIPDUB_API_KEY=your_key_here -- npx -y lipdub-mcp{
"mcpServers": {
"lipdub": {
"command": "npx",
"args": ["-y", "lipdub-mcp"],
"env": { "LIPDUB_API_KEY": "your_key_here" }
}
}
}The file lives at ~/Library/Application Support/Claude/claude_desktop_config.json on
macOS, and %APPDATA%\Claude\claude_desktop_config.json on Windows. Restart Claude
Desktop after editing it.
{
"mcpServers": {
"lipdub": {
"command": "npx",
"args": ["-y", "lipdub-mcp"],
"env": { "LIPDUB_API_KEY": "your_key_here" }
}
}
}{
"inputs": [
{ "type": "promptString", "id": "lipdub-key", "description": "LipDub API Key", "password": true }
],
"servers": {
"lipdub": {
"command": "npx",
"args": ["-y", "lipdub-mcp"],
"env": { "LIPDUB_API_KEY": "${input:lipdub-key}" }
}
}
}This pattern keeps the key out of a file you might commit. Given that LipDub issues one key per user, that matters more here than it does for most servers.
{
"mcpServers": {
"lipdub": {
"command": "npx",
"args": ["-y", "lipdub-mcp"],
"env": { "LIPDUB_API_KEY": "your_key_here" }
}
}
}[mcp_servers.lipdub]
command = "npx"
args = ["-y", "lipdub-mcp"]
env = { LIPDUB_API_KEY = "your_key_here" }git clone https://github.com/marzvfx/lipdub-mcp.git
cd lipdub-mcp
npm ci && npm run buildThen use "command": "node", "args": ["/absolute/path/to/lipdub-mcp/dist/index.js"]
instead of the npx pair above. Needs Node 20+; ./manage.sh build does the same
inside a container if you have no Node.
3. Check it works
First confirm the server itself runs:
npx -y lipdub-mcp --versionThen ask your agent: "check my LipDub connection". It should reply with your
account id. If it asks you to set an API key, the key is not reaching the server.
Check the env block above and restart the client.
Or test it without an agent
There is a smoke test that drives the published server exactly as a real MCP client does, so a pass means the thing you installed actually works. No clone:
LIPDUB_API_KEY=your_key_here npx -y lipdub-mcp --smokeThat checks the handshake, the tool list and your API key. It renders nothing and costs nothing.
To exercise the whole flow, including a real render. This spends credits:
LIPDUB_API_KEY=your_key_here npx -y lipdub-mcp --smoke --render \
--video=https://example.com/speaker.mp4 \
--audio=https://example.com/speech.mp3It starts the render, waits for it, and prints the download link.
If you already have assets uploaded to LipDub, pass their ids instead of URLs with
--video-id=<shot id> --audio-id=<upload id>, which helps when you have nowhere public
to host them. If a run is interrupted, --render-id=<id> re-attaches to the render
already in flight rather than paying for a second one.
From a git checkout, npm run smoke is the same command.
Tools
Tool | What it does | Costs credits? |
| Confirms the key works and names the account | No |
| Starts a render from a video URL and an audio URL | Yes |
| Status, and the download link once ready | No |
| Waits for a render instead of polling in a loop | No |
| Recent renders, to recover a lost render id | No |
There is also a lipdub_quick_dub prompt (a slash command in clients that support
prompts) and two reference resources, lipdub://guide/quickstart and
lipdub://guide/troubleshooting.
What makes a good source video
One person on camera.
Face clearly visible and reasonably well lit.
Accepted | |
Video |
|
Audio |
|
Size | up to 15 GB per file, or 5 GB on the legacy Basic plan |
Hosting your files
Both inputs are public URLs, not local file paths, and the link must return the media file itself.
YouTube is the exception: youtube.com, youtu.be and youtube-nocookie.com are
resolved for you, so a normal watch URL works as video_url.
These do not work: Google Drive and Dropbox share pages, anything behind a login, and expired links.
For your own files:
# Amazon S3 — a time-limited direct link
aws s3 presign s3://your-bucket/keynote.mp4 --expires-in 3600
# Google Cloud Storage
gcloud storage sign-url gs://your-bucket/keynote.mp4 --duration=1h
# Any web server
scp keynote.mp4 you@yourserver:/var/www/html/
# → https://yourserver/keynote.mp4Why URLs and not local files? A URL-based render is one API call. The upload-a-local-file path is several calls against endpoints that are rate-limited to roughly ten requests an hour, which works out at about two local-file renders per hour on the entry plan. Keeping this server URL-only also means it behaves identically wherever it runs. Local-file support may arrive later behind an opt-in.
Timing and cost
Rendering takes several minutes for a short clip, and longer for longer videos, because generation time scales with the length of the source. LipDub also waits up to 15 minutes to download your two source files before giving up, so slow hosting shows up as a timeout rather than a render.
Renders consume credits from your LipDub account and cannot be refunded; cost scales with the length of the source video, so shorter clips cost less.
Checking status is free and is not rate-limited, so poll as often as you like.
Your credit balance is not available through the API; see app.lipdub.ai.
Spending guardrails
lipdub_create_render is the only tool that spends money. By default it refuses to run
until the agent passes confirm_spend, which forces it to state the cost to you first.
The server also stops after 5 renders per session.
Variable | Default | Purpose |
| — | Your API key. Required. |
| — | Path to a file containing the key, instead of the variable. |
|
| Ceiling on renders started per server process. |
|
| Set |
|
|
|
These are usability guardrails, not security controls. Anything calling the API directly bypasses them.
Troubleshooting
Symptom | Fix |
"No LipDub API key is configured" | Generate one at Settings → API Keys and set |
"LipDub rejected the API key" | The key was mistyped or has been regenerated. Generate a fresh one and restart the client |
"out of credits" | Top up at app.lipdub.ai |
"could not download one of your source files" | The link is a share page, needs a login, or has expired. Use a direct link |
"downloaded your files but could not start the render" | Usually no credits, or no clearly visible speaking face |
Wait tool returned | Normal — the render is still going. Not a failure; call it again |
Download link stopped working | Links are signed and short-lived. Call |
Full API documentation: lipdub.readme.io
Privacy
Your API key is read from the environment, is used only to call
https://api.lipdub.ai, and is never written to disk, logged, or included in any tool result. It is redacted from every log line and error message.The video and audio URLs you supply are sent to the LipDub API, which downloads them to produce the render. Do not pass URLs to material you are not willing to have LipDub process.
This server sends no telemetry and collects no analytics.
Renders and their outputs are stored in your LipDub account, governed by LipDub's privacy policy and terms.
Acceptable use
LipDub 2 generates synthetic video of real people. By using it you warrant that you have the rights and consent necessary for the likeness and the voice in your source material. Do not use it to impersonate anyone without their permission, to create misleading content about real people, or for anything prohibited by LipDub's terms.
Security
Found a vulnerability? See SECURITY.md. Please do not open a public issue, and never paste an API key into one.
Limitations
No translation or speech generation. You supply the audio.
URL inputs only; no local file upload.
Credit balance and price estimates are not available through the API.
LipDub issues one API key per user, so a key cannot be scoped to this server alone.
Versioning
Tool names are a public contract and will not change. New capability arrives as new tools; schema changes are additive. See CHANGELOG.md.
Licence
MIT. This licence covers this client. LipDub itself is a commercial service governed by its own terms.
Available Tools
5 toolslipdub_check_connectionCheck LipDub connectionARead-onlyIdempotent
Verify that this server can reach LipDub and report which account it is using.
Call this first if any other LipDub tool fails with an authentication error, or when a user asks whether LipDub is set up.
Does not use credits and does not start anything.
Credit balance is not available through the API — direct the user to https://app.lipdub.ai to see it.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive, but the description goes further: it states that the tool does not use credits and does not start anything, and it discloses that credit balance is not available via the API, directing users to the web app. These are behavioral details beyond what annotations capture, providing real value for an agent deciding whether to call it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three short paragraphs, each with a distinct purpose: purpose, when to use, and notes. Every sentence earns its place with no filler. The key purpose is front-loaded, and the caveats are placed last without bloating the text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter health-check tool with no output schema, the description fully covers what an agent needs: what it does, when to use it, what it doesn't affect, and an important limitation (credit balance). The sibling tools are all about renders, so this check tool is clearly distinct. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is nothing to describe. The schema coverage is trivially 100%. Per the baseline rule, a score of 4 is appropriate because the description adds no parameter-specific info, but none is needed. The description correctly avoids inventing unnecessary parameter explanations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a concrete verb+resource: 'Verify that this server can reach LipDub and report which account it is using.' This is unambiguous and clearly distinguishes it from the sibling render-management tools. Even without seeing the siblings, the tool's role is immediately understandable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to call it: 'Call this first if any other LipDub tool fails with an authentication error, or when a user asks whether LipDub is set up.' It also clarifies what it does NOT do (uses credits, starts anything), which prevents misuse. This is textbook usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lipdub_create_renderCreate LipDub render (uses account credits)A
Start a LipDub 2 render: make the person in a source video appear to speak a different audio track, with matched lip movement.
USES CREDITS. Each render charges the account and cannot be undone or refunded. Cost scales with the length of the source video. Confirm with the user first, then call again with confirm_spend set to true.
Supply both sides as direct, publicly downloadable URLs:
video_url — a video of ONE person speaking, face clearly visible.
audio_url — the audio you want them to appear to say.
LipDub 2 does NOT translate, transcribe or generate speech. If the user wants another language, produce that audio first with a separate text-to-speech or voice tool, host it at a URL, and pass that URL as audio_url.
Links must return the media file itself, with two exceptions: YouTube links work (youtube.com, youtu.be or youtube-nocookie.com), because they are resolved for you. Google Drive and Dropbox share pages, and anything behind a login, will fail.
Returns immediately with a render_id — the render is NOT finished. Rendering takes several minutes for a short clip, and longer for longer videos. Next step: call lipdub_wait_for_render with that render_id.
| Name | Required | Description | Default |
|---|---|---|---|
| audio_id | No | Advanced. A LipDub audio upload id from a previous upload, instead of audio_url. | |
| video_id | No | Advanced. A LipDub shot id from a previous upload, instead of video_url. | |
| audio_url | No | Direct URL to the audio the person should appear to say (.mp3, .wav, .m4a, .aac, .ogg, .flac, .mp4 or .mov). You must supply finished audio; LipDub 2 does not generate or translate speech. | |
| video_url | No | URL to the source video (one person, face visible). .mp4, .mov or .avi, or a YouTube link. | |
| callback_url | No | Optional https URL to be called when the render finishes. For automated pipelines; in a chat session use lipdub_wait_for_render instead. | |
| confirm_spend | No | Whether the user has personally approved spending credits on this render. Defaults to false deliberately, as a safety gate — it is not a misconfiguration. Set it to true only after you have asked the user and they have agreed. Setting it yourself without asking spends their money without consent. | |
| output_filename | No | Optional name for the finished file, e.g. 'ceo-welcome-spanish.mp4'. If omitted, a name is derived from video_url. This name is also shown as the project name in the user's LipDub web app, so make it meaningful. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Goes well beyond the annotations by disclosing financial consequences ('USES CREDITS', 'cannot be undone or refunded'), cost scaling with source video length, asynchronous behavior (returns render_id immediately, render not finished), and typical render duration. No contradiction with the annotations; this description adds substantial behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the most critical information (purpose and cost warning), then organized into clear paragraphs and bullet lists covering inputs, exclusions, and post-call behavior. Every sentence earns its place; the length is justified by the tool's high-stakes, asynchronous, and easily-misused nature.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description fully explains what the caller should expect: an immediate render_id, that rendering is asynchronous, how long it may take, and the required next step. It also covers prerequisites, URL constraints, known failure modes, and the spending-confirmation gate, making it complete for correct invocation despite the schema listing all parameters as optional.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Even though schema coverage is 100%, the description adds crucial meaning: URLs must be direct, publicly downloadable media files; YouTube links are resolved but Google Drive/Dropbox share pages and login-protected links fail; video must show one person with a clear face; audio must be finished because LipDub does not generate speech. This meaningfully enriches the schema's parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Start a LipDub 2 render') and a clear resource with the intended effect: making the person in a source video appear to speak a different audio track with matched lip movement. This clearly distinguishes it from siblings like lipdub_list_renders, lipdub_get_render, and lipdub_wait_for_render, none of which create a render.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use and when-not-to-use guidance: confirms credits must be approved by the user first, instructs to call again with confirm_spend=true, warns that LipDub 2 does not translate/transcribe/generate speech, and routes follow-up to lipdub_wait_for_render. It also explains when callback_url is appropriate versus chat-session use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lipdub_get_renderCheck LipDub render statusARead-onlyIdempotent
Check a LipDub render once, right now, and return its download link if it has finished. This does NOT wait — it answers immediately with whatever the current status is. To wait for a render to finish, use lipdub_wait_for_render instead.
Takes the render_id from lipdub_create_render.
Free and never rate-limited — check as often as you need.
status is one of:
preparing — LipDub is downloading your source files (usually under 2 minutes)
queued — waiting for a render slot
rendering — generating the video
succeeded — done; download_url is included
failed — see failure_reason
download_url is a temporary signed link that expires. Give it to the user or download it promptly; do not save it for later.
If the render is not finished yet, prefer lipdub_wait_for_render — it waits for you instead of making you check repeatedly.
| Name | Required | Description | Default |
|---|---|---|---|
| render_id | Yes | The render_id from lipdub_create_render. A plain number is also accepted. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint and idempotentHint, and the description layers genuinely useful context on top: the immediate-non-waiting behavior, the full status enum with meanings, and the critical caveat that download_url is a temporary signed link that must be handed to the user or downloaded promptly. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core single-check purpose and the critical non-waiting distinction, followed by usage, rate limits, status enum, and URL handling in logical order. It is slightly verbose — the status enum and expiry caveat could theoretically live in an output schema — but every section earns its place with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no output schema, the description covers everything an agent needs: the status value set, what download_url contains and its expiry behavior, how render_id is obtained, and routing to the wait sibling. Nothing required for a correct call is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema already documents render_id thoroughly ('The render_id from lipdub_create_render. A plain number is also accepted.'). The description just restates the source ('Takes the render_id from lipdub_create_render'), adding little beyond the schema. Baseline 3 is appropriate since the schema carries the load.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a precise verb-resource pair — 'Check a LipDub render once, right now' — and distinguishes itself from lipdub_wait_for_render by clarifying what it does NOT do ('does NOT wait'). This makes it trivially distinguishable from its siblings without opening any schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when-to-use and when-not-to-use guidance is given in two places: 'To wait for a render to finish, use lipdub_wait_for_render instead' and the closing 'If the render is not finished yet, prefer lipdub_wait_for_render'. It also states the unthrottled nature ('Free and never rate-limited — check as often as you need'), giving the agent a clear green light for polling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lipdub_list_rendersList recent LipDub rendersARead-onlyIdempotent
List recent LipDub renders on this account, newest first.
Use this when:
you have lost a render_id, for example because earlier conversation was trimmed away and a render is still running or already finished
the user asks what they have rendered recently
Returns a short summary of each. Call lipdub_get_render with one of the returned render_ids to get its download link.
Free and never rate-limited.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many renders to return. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable context: it is account-scoped, returns 'a short summary of each', and is 'Free and never rate-limited'. This goes beyond the annotations by disclosing cost and rate-limit behavior, which is genuinely useful for an agent deciding whether to call it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear main statement, bullet-pointed 'when to use' triggers, and a note about returns and cost. It is slightly longer than strictly necessary, but every section serves a purpose and no sentence is wasted. The main action is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with one optional parameter and no output schema, the description covers the essential information: what it returns (short summary), how to proceed to get a download link (via lipdub_get_render), and cost/free status. It does not describe the exact shape of the summary, but that is not critical for invoking the tool. Overall it is sufficient for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'limit' is fully documented in the input schema (type, default, min, max, description). The tool description adds no additional semantics beyond the schema — it does not mention pagination limits or default behavior. With 100% schema coverage, the baseline of 3 is appropriate; the description does not need to repeat schema details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List'), a resource ('LipDub renders'), and a scope ('on this account'), plus an explicit ordering ('newest first'). It clearly distinguishes from siblings like lipdub_get_render (retrieve one) or lipdub_create_render (make new), so an agent can immediately identify the tool's role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'Use this when' conditions: losing a render_id (e.g., after conversation trimming) or when the user asks for recent renders. It also advises the follow-up call to lipdub_get_render, giving clear decision guidance about when this tool is appropriate and what to do next.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lipdub_wait_for_renderWait for LipDub render to finishARead-onlyIdempotent
Wait for a LipDub render to finish, then return its download link. Use this straight after lipdub_create_render instead of checking in a loop — it uses far less context.
This call blocks for up to max_wait_seconds (default 45, maximum 600) while tracking the render for you. A render takes several minutes for a short clip, and longer for longer videos, so expect to call this several times — that is normal and costs nothing.
If the render is still going when the wait is up, this returns normally with still_running set to true. That is NOT an error and NOT a failure — the render is fine and is still going. When it happens: tell the user it is still in progress, give them the render_id so they can come back to it, and call this tool again to keep waiting.
Waiting and status checks are free and never rate-limited.
| Name | Required | Description | Default |
|---|---|---|---|
| render_id | Yes | The render_id from lipdub_create_render. | |
| max_wait_seconds | No | How long to wait before returning. Returning early with still_running set to true is normal, not a failure. The default of 45 stays under the 60-second request timeout most MCP clients use; only raise it if your client is configured to wait longer. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, but the description adds critical behavioral detail beyond these: the blocking nature, the max_wait_seconds cap, the normal return with still_running, and the explicit statement that it is not an error. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently organized: purpose first, then usage, then behavior, then user guidance. Every sentence earns its place—no fluff. It front-loads the core purpose and the key differentiator, and the length is appropriate for the complexity of the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description fully covers what an agent needs: how to call it, what to expect (including partial results), how to handle still_running, and cost/rate-limit implications. It leaves no gaps for correct invocation and follow-up.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (both parameters documented). The description adds context beyond the schema, such as explaining that renders take several minutes and it's normal to call repeatedly, which informs how to set max_wait_seconds. However, the schema already covers the key semantics of max_wait_seconds and still_running, so the description's extra value is limited but present.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a precise verb-resource pair: 'Wait for a LipDub render to finish, then return its download link.' It explicitly differentiates from alternatives by stating to use it 'straight after lipdub_create_render instead of checking in a loop,' making the tool's purpose unambiguous and distinct from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance: 'Use this straight after lipdub_create_render instead of checking in a loop.' It also instructs what to do when still_running is true: tell the user, provide the render_id, and call again. It even notes that repeated calls are normal and free, covering both timing and cost expectations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.1.1- Changed
lipdub_create_render3 fields changed- changed
Input schema / properties / audio_url / descriptionPrevious value: -"Direct https URL to the audio the person should appear to say. You must supply finished audio; LipDub 2 does not generate or translate speech."New value: +"Direct URL to the audio the person should appear to say (.mp3, .wav, .m4a, .aac, .ogg, .flac, .mp4 or .mov). You must supply finished audio; LipDub 2 does not generate or translate speech." - removed
Input schema / properties / transcriptRemoved value: -{ - "description": "Optional. The words spoken in audio_url. Supplying this can improve lip-sync accuracy.", - "maxLength": 4096, - "type": "string" -} - changed
Input schema / properties / video_url / descriptionPrevious value: -"Direct https URL to the source video (one person, face visible). .mp4, .mov, .avi or .webm."New value: +"URL to the source video (one person, face visible). .mp4, .mov or .avi, or a YouTube link."
- Changed
lipdub_wait_for_render2 fields changed- changed
Input schema / properties / max_wait_seconds / defaultPrevious value: -240New value: +45 - changed
Input schema / properties / max_wait_seconds / descriptionPrevious value: -"How long to wait before returning. Returning early with still_running set to true is normal, not a failure."New value: +"How long to wait before returning. Returning early with still_running set to true is normal, not a failure. The default of 45 stays under the 60-second request timeout most MCP clients use; only raise it if your client is configured to wait longer."
5 tool updates
v0.1.0- First observed
lipdub_check_connection - First observed
lipdub_create_render - First observed
lipdub_get_render - First observed
lipdub_list_renders - First observed
lipdub_wait_for_render
TDQS
Scored across 5 tools
Each tool has a clearly distinct role: connection check, listing, creating, one-shot status check, and blocking wait. The overlap between get_render and wait_for_render is explicitly disambiguated in their descriptions.
All tools follow the same lipdub_ prefix with a consistent verb_noun pattern: check_connection, list_renders, create_render, get_render, wait_for_render. There are no naming style deviations.
Five tools is well-scoped for this server's purpose. Each tool covers a necessary part of the render workflow without redundancy or bloat.
The tool surface covers the full render lifecycle: verify setup, create a render, wait for completion, retrieve the result, and list past renders. No obvious missing operation is needed for the stated domain.
Maintenance
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