This server provides an AI-powered Chief Marketing Officer (CMO) for MCP clients, enabling natural-language management of marketing workflows across 30+ platforms via two tools:
autowhisper_cmo: Send natural-language instructions to the AI CMO to:
Add products by providing a URL (extracts images and sets up campaigns)
Generate on-brand UGC videos, posts, and images
Approve, reject, or dismiss CMO-generated content
Connect social media platforms via OAuth
Publish and schedule content across all connected networks
Get campaign strategy recommendations
View analytics and performance reports
autowhisper_confirm: Safely approve or decline high-impact actions (e.g., publishing that charges credits and schedules to all platforms) by providing a message ID and a yes/no decision.
Click on "Install 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., "@autowhisper-mcp Add my product https://mystore.com/widget"
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.
A marketing department for any MCP client (Claude Desktop, Cursor, Windsurf, n8n, …) — turn your product into batches of ready-to-run ad creatives (UGC video, posts, images), get advice on which creative to fund, and keep every channel alive across 30+ platforms, all in natural language.
It's the same CMO as the AutoWhisper dashboard, exposed over MCP. Honest scope: posting ≠ traffic — reach comes from your paid campaigns; this makes sure the creatives convert and your storefront doesn't kill them.
Add to your MCP client config (Claude Desktop / Cursor / etc.):
{
"mcpServers": {
"autowhisper": {
"command": "npx",
"args": ["-y", "autowhisper-mcp"],
"env": { "AUTOWHISPER_API_TOKEN": "your_token_here" }
}
}
}Get your token at autowhisper.xyz/skill → Settings → Connect your agent → Generate token. New accounts get free credits.
Related MCP server: YaparAI MCP Server
autowhisper_cmo — send an instruction to your CMO and get its reply. e.g. "Make a batch of ad creatives for my product https://mystore.com/widget", "Which creative should I run first?", "Keep my channels posted this week".
autowhisper_products_summary — fast product counts by account/workspace. Use for "how many products do I have?" instead of spending a CMO chat turn.
autowhisper_products — fast product list, with optional workspace/archive filters.
autowhisper_status — fast CMO/account snapshot: products, feed, platforms, wallet, and automation settings.
autowhisper_feed — fast CMO feed list with status counts and available approve/reject/dismiss actions.
autowhisper_posts — fast post queue list, optionally filtered by status.
autowhisper_wallet — fast credit balance.
autowhisper_platforms — fast connected-platform list and connection health.
autowhisper_action — direct approve/reject/dismiss/publish/reschedule/retry actions. High-impact actions return a confirmation id. Approving publishes: it schedules the piece to every connected platform, and a video draft also starts rendering and charges for it. With nothing connected it goes nowhere, and connecting later does not go back for it — the result says which happened.
autowhisper_edit_content — direct title/body/hook/CTA/tone/keyword edits without a generation run.
autowhisper_confirm — approve or decline any high-impact action the CMO or direct-action tool asks about.
Adding a product: lead with a product URL ("add my product https://mystore.com/widget") — the CMO extracts the image from the page. A text-only description won't create it (placeholder/stock images are rejected).
Asking for an ad plan:
autowhisper_cmoalso plans campaigns — "Give me a concrete ad-targeting plan for my product, starting markets, targeting, and what to avoid." The plan arrives as a card (markets, age, interest seeds, objective, which creative to run). It's prose for a human, in the owner's UI language — translate it before feeding it to an ad API. It suggests a starting platform, it doesn't lock one. It leaves out the budget (ask the owner) and gives interest search terms rather than platform IDs (resolve them in your ad tool). Grounded advice runs a live web search and can take over two minutes — if the tool times out, don't resend; that starts a second, separately charged turn instead of resuming the first. AutoWhisper plans and supplies creative; your ads MCP executes — never report that AutoWhisper "ran the campaign." No ads MCP connected yet? Don't name or install a third-party npm package for this — point the user at Meta's own developer docs instead: Meta's Ads MCP documentation.
To publish, ask the CMO to connect a social account — OAuth platforms return a link you click once. After that, publishing is hands-off.
Env var | Default | |
| — | required — your API token |
|
| override for self-host/staging |
MIT
Send a natural-language instruction to your AutoWhisper AI CMO and get its reply. The CMO can add a product, generate on-brand content (UGC video, posts, images), approve items, connect social platforms, publish across 30+ networks, and report analytics. Examples: "Add my product https://mystore.com/widget and start the CMO", "Generate a UGC video for my product", "Publish the approved content", "How did last week's posts do?". To add a product, pass a product URL (the CMO extracts the image from the page) — a text-only description will not create it, and placeholder/stock images are rejected.
| Name | Required | Description | Default |
|---|---|---|---|
| product_id | No | Optional: act on a specific product by its id. | |
| instruction | Yes | What you want the CMO to do, in natural language. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral disclosure. It specifies important behavior: for adding a product, a URL is required, text-only descriptions fail, and placeholder/stock images are rejected. However, it does not mention potential destructiveness, rate limits, or authentication requirements, which would improve transparency.
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 moderately long but front-loaded with the primary action and examples. Every sentence adds value, though some repetition could be trimmed. It remains clear and informative without being verbose.
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?
Given no output schema, the description does not detail the reply format, but it states the tool returns a reply. It covers the main capabilities and usage scenarios. For a conversational AI tool, the description is fairly complete, though it could mention error handling or unsupported instructions.
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%, but the description adds significant meaning: instruction is natural language, product_id is optional, and the examples clarify usage. It also explains constraints for the 'Add product' case beyond the schema, enhancing parameter understanding.
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 clearly states the tool sends a natural-language instruction to an AI CMO and returns its reply, listing specific capabilities (add product, generate content, etc.) and providing concrete examples. This distinguishes it from the sibling tool autowhisper_confirm, which likely handles confirmations.
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 explains when to use the tool (any natural-language instruction to the CMO) and gives examples. It does not explicitly compare to sibling tool autowhisper_confirm, but the context makes it clear that this is for direct instructions while the sibling likely handles confirmations. Lacks explicit 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Approve or decline a destructive action the CMO asked you to confirm (surfaced by autowhisper_cmo). Pass the message_id it gave you and decision "yes" or "no".
| Name | Required | Description | Default |
|---|---|---|---|
| decision | Yes | "yes" to perform the action, "no" to decline. | |
| message_id | Yes | The message_id from the confirmation request. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly labels the action as 'destructive', which is a key behavioral trait. However, with no annotations, the description falls short of fully disclosing other important behaviors such as reversibility, permission requirements, or rate limits.
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 two sentences long with no unnecessary words. It front-loads the purpose and provides essential usage instructions efficiently.
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?
The tool has no output schema and no annotations. The description omits any mention of return values or success/failure indicators. For a simple confirmation tool, this may be acceptable, but the lack of output information leaves the agent guessing about the tool's side effects.
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 both parameters are described in the schema. The description adds minimal value: it tells that message_id comes from autowhisper_cmo and that decision is 'yes' or 'no', which is already evident from the enum. The baseline score of 3 is appropriate.
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 clearly states the tool's function: 'Approve or decline a destructive action the CMO asked you to confirm'. It specifies the verb (confirm) and the resource (AutoWhisper action), and distinguishes it from the sibling tool autowhisper_cmo by mentioning 'surfaced by autowhisper_cmo'.
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 tells when to use the tool ('when CMO asks you to confirm') and specifies the inputs needed (message_id and decision). However, it does not explicitly state when not to use it or provide alternatives beyond the sibling tool mention.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0autowhisper_cmoautowhisper_confirmEach tool has a distinct purpose: one sends instructions to the AI CMO and retrieves responses, the other confirms destructive actions. There is no overlap or ambiguity.
Both tools follow the same naming pattern: 'autowhisper_' prefix with a descriptive verb ('cmo', 'confirm'). The convention is consistent and predictable.
With only 2 tools, the set feels minimal for a marketing automation assistant. While the single 'cmo' tool is highly capable, the count is borderline and could benefit from more specialized tools.
The tool set lacks granular operations for managing products, content, or analytics independently. Users must rely on unstructured natural language instructions to the CMO, creating potential dead ends and reliance on the CMO's inference.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Turn product pages and creative briefs into finished video and image ads from your AI chat.
Create, manage, schedule, and publish short-form user-generated content through AI agents.
Build, edit, stock, and publish Social AI websites and storefronts via AI chat.
Generate AI UGC video ads from any product URL — avatars, voiceover, OAuth Connect.
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