Claude Code + Higgsfield MCP = Content MACHINE

CChase AI
컴퓨터/소프트웨어마케팅/광고창업/스타트업AI/미래기술

Transcript

00:00:00Claude code has a content creation problem and Higgs Field's brand new MCP
00:00:04server just solved it instead of having to individually connect every AI content
00:00:09creation tool to Claude code,
00:00:10which you kind of have to do since the best ones change from week to week,
00:00:14we can now get them all in one place via the MCP server.
00:00:17And this just isn't a convenience win.
00:00:19This means we can now reliably automate large portions of our content creation
00:00:24process with the best AI tool for the job.
00:00:28So today I'm going to show you exactly how do you install this tool and the
00:00:31process I use to create this piece of content.
00:00:34I got a hundred thousand views in less than 24 hours using the Higgs Field MCP
00:00:39server. So why does the Higgs Field MCP server even matter? Why should you care?
00:00:43Well, I alluded to it in the intro and there's two reasons.
00:00:45First one is the fact that we now have a single
00:00:50pathway to getting access to every single AI content creation
00:00:55tool. Because up until now, we really haven't been able to do that programmatically.
00:00:59Instead you had to individually connect all these tools to Claude
00:01:04giant pain in the butt.
00:01:05Nobody did this because everyone had its own API, its own payments,
00:01:10even if the API was publicly available, which in some cases it wasn't.
00:01:14And so you were kind of locked into the one or two you actually use.
00:01:18Problem with that is the best ones change all the time. Last week,
00:01:23nano banana pro was on top. Guess what? Now it's GPT images too.
00:01:27Six months ago, VU three was the top dog a month ago. It was clean.
00:01:31And today it's seed dance. Are you using the best tool for the job?
00:01:35Chances are you weren't if you were set up like this,
00:01:37but now all I have to do is be connected to the Higgs Field MCP
00:01:42and boom, I can now connect to all of these and more.
00:01:47And by more, I mean, there's 17 image models, 14 video models,
00:01:52and we have access to a lot of the Higgs Field proprietary models,
00:01:56but the real unlock isn't the convenience.
00:01:58It's the fact that since it's an MCP server,
00:02:00we can now automate a lot of these processes via Claude code. For example,
00:02:05I can create an automation where every single day,
00:02:08Claude code takes a look at GitHub and it says, Hey,
00:02:11what are the top trending AI repos for this week for this month,
00:02:16brand new ones that just got released. It's going to take that information.
00:02:20It's going to bring it back inside of Claude code.
00:02:22And it's now going to structure it in a way that I could use for some sort of
00:02:25social media posts. In our example, it will be a carousel alongside that.
00:02:30It will then create a prompt for the images so that we can get images
00:02:35like these,
00:02:36but it has the copy and the information from the GitHub that it just pulled.
00:02:40It's thinking to send all that information to Higgs Field,
00:02:43which will then call on GPT images to, to create all that for us.
00:02:47It then brings it back into Claude code and voila.
00:02:51We just have a completely automated content creation process from there.
00:02:54I can manually review them. I can have Claude code post them,
00:02:57but the point is I can now automate some sort of flow.
00:03:01You could automate even more of it where I'm grabbing information from some
00:03:05outside place. In this case, GitHub,
00:03:07I'm then analyzing the information side of Claude code.
00:03:10I'm taking that analyzed information and I'm turning it into some sort of content
00:03:14prompt, which gets sent to the Higgs Field MCP. And then it brings it all back to me.
00:03:18And I have a nice deliverable and I really haven't even lifted a finger.
00:03:21So that's the real power that is unlocked via this MCP server.
00:03:25So you combine these two things and we are really turning Claude code into a
00:03:29marketing machine. So let's talk about the install. First of all,
00:03:32you are going to need a Higgs Field account.
00:03:34I will have a link to that in the description. If it wasn't clear by now,
00:03:37Higgs Field is a one-stop shop for all things, AI content creation related.
00:03:42Next, we need to install the MCP. And there's really two ways to do this.
00:03:47One, we can go inside claud.ai and just set up a connector.
00:03:51Two, we can just do it inside the terminal via Claude code.
00:03:55Setting up the connector is very easy. You'll just go to claud.ai.
00:03:58You'll head to settings. You will go to connector.
00:04:01You will go to add custom connector. You'll copy this,
00:04:06paste that in there and hit add.
00:04:09You'll then hit connect and it will ask you to log in.
00:04:12And boom, there we go.
00:04:14I can now essentially call any of these audio video tools,
00:04:19image tools that live inside of Higgs Field from Claude itself, the web app.
00:04:23And I can also do it from the desktop app. So inside the chat, I said,
00:04:26use the Higgs Field connector and create an image talking about the power of
00:04:29Claude code plus Higgs Field using GPT image too.
00:04:32And you can see it's calling the model right now.
00:04:36It will ask you for some permissions.
00:04:37You can see the actual prompt it's sending in JSON and we see the image in
00:04:42progress.
00:04:42The nice thing about doing this inside of the actual chat bot
00:04:47application or on your desktop,
00:04:49Claude app is the fact that the images will be generated in line,
00:04:52which means I'm actually going to be able to see them. And remember,
00:04:54there's a lot more we can do than just simply create an image or a video.
00:04:58There's actually a lot going on under the hood with this MCP.
00:05:01You can ask Claude itself to explain it to you,
00:05:04but I also have this guide that I wrote up that I will put inside of the free
00:05:07school community. There will be a link to that in the description.
00:05:10And here's the image it created for us. And as I scroll over it,
00:05:13you can see have a few options. I can recreate it.
00:05:16So essentially just send a prompt there again, I can animate it.
00:05:19So send it to a video editor. I can edit it.
00:05:22And so what it does is it brings up essentially another prompt. In this case,
00:05:26it would send it to nano banana too, but I could change that to, you know,
00:05:29like GPT image too.
00:05:32It links the reference image so it knows what it's editing.
00:05:36And then you just put your prompt in there.
00:05:37So pretty intuitive in terms of how you mess with this inside of
00:05:42the chat application,
00:05:43but let's talk about what I think is the biggest unlock and that's using it
00:05:46inside of Claude code. So to set up the MCP server inside of Claude code,
00:05:50super simple as well,
00:05:51literally just going to use plain language and say set up this MCP server for me.
00:05:56You'll just head back to this page, which is the Higgsfield MCP page.
00:05:59I'll link that as well. We'll do custom connector.
00:06:03You'll paste it in there and it's going to go to work.
00:06:06It's going to set it up for you.
00:06:07And it will also give you a link to go through the same authentication process.
00:06:10You just saw me do on the web app now to confirm it's set up,
00:06:13just do forward slash MCP. And you should see Higgsfield connected.
00:06:17If you don't just have a back and forth with Claude code,
00:06:20and we'll walk you through the steps to make sure it's connected.
00:06:22You may just need to exit cloud code and spin it back up. Now at this point,
00:06:26once the MCP server is connected,
00:06:28we can now use basically any AI content creation model from
00:06:34the terminal through natural language.
00:06:36So if I tell Claude code create me 16 different images
00:06:41with GPT images too, it will do that for you.
00:06:44And it will just download them and you can even tell it, Hey,
00:06:47I want you to bring up the images for me.
00:06:48The only downside with the terminal is obviously we can't see the images inside
00:06:52the terminal itself, but Hey,
00:06:55what we really want to do is we want to figure out how to put this inside of an
00:06:58automation, how to script it.
00:06:59But natural language prompting is simple and exact same process as I
00:07:04showed you on the web app.
00:07:05So let's go through this process.
00:07:07So what we first need is we need to be able to grab information from GitHub and
00:07:11bring it back into cloud code. And you can see that right here.
00:07:13This is an automation that runs every single morning,
00:07:15and it grabs the top 10 trending GitHub repos this week that were
00:07:20created in the last seven days and ranked them based on stars.
00:07:24It gives me a quick description, all this sort of stuff.
00:07:26And I can also see the top five trending over the last month. And again,
00:07:30these are just new ones that are just on the scene.
00:07:32Now to create this for yourself is actually really simple.
00:07:35I have the whole breakdown inside of chase AI plus,
00:07:38but you can literally just prompt cloud code and say, Hey,
00:07:40can you create me an automation that checks GitHub for this every single day?
00:07:44There's no API you need to set up or anything like that.
00:07:46But what I want to do is I want cloud code to take a look at this information and
00:07:51I want it to essentially turn it into a carousel.
00:07:55And if you're not familiar with carousels, they're just posts like this.
00:07:58We'll have some sort of cover page. This one is top five cloud code front end skills,
00:08:02but instead we'll do top five log code, GitHub repos or top five AI repos.
00:08:07We'll see what cloud code comes up with.
00:08:09I'm going to give it the reference images that you see here.
00:08:11So I'll give it the cover page and I will give it some of these, you know,
00:08:15body slides, so to speak,
00:08:16because I'm going to want it to be in the same sort of style and we'll see what
00:08:20it comes up with. So I'll feed it this,
00:08:23I'll feed it the GitHub information and then cloud coach and say, okay,
00:08:27based on everything in this GitHub, based on the reference images,
00:08:31here's what we should think about in terms of coming up with a prompt.
00:08:34So I gave cloud code a pretty simple prompt. I said,
00:08:36take a look at our GitHub trending data for today.
00:08:39What I just showed you inside of obsidian.
00:08:41I want to create a carousel talking about that information.
00:08:44We could call it top five trending AI repos this month or something like that.
00:08:48I want it turned into slides like this cover plus body slides.
00:08:52And then I fed them those four slides or those three slides. And then I just said,
00:08:57let's talk about it before sending it off for content creation.
00:08:59Now what we're doing here is we're sort of manually going through each
00:09:04step. So we already did the content.
00:09:07Now we're going to talk about it before we send it off here to Higgs field.
00:09:10What you would actually want to do after you kind of got this to a place you liked
00:09:15and you've continued doing this over and over is instead of me being all right,
00:09:19now let's do GitHub. Now let's talk about it. Now let's push the prompts.
00:09:23We could actually turn this entire thing into like one large call it.
00:09:27You could call it like Higgs field skill, or really any skill.
00:09:31You want to give it whatever name you want,
00:09:33but you can automate this entire process and you could have something that every
00:09:37single day, you know, right after it hits GitHub in the morning and says, Hey,
00:09:42here's the top 10 repos. Well, why don't we just turn that into a post?
00:09:45You could have a carousel every single day that says, Hey,
00:09:47here are the top 10 trending AI repos for today.
00:09:52You know,
00:09:53that's actually like somewhat relevant content that people would actually like,
00:09:56and this is an easy way to create it. I don't see my idea.
00:10:00So Claude is telling us, Hey, I pulled today's trending files.
00:10:03It's just repeating the top five GitHub repos for this month.
00:10:07It has some thoughts. Claude code is suspicious. Yeah. Yeah. Little sketch.
00:10:13Talks about the hook angle talks about the title
00:10:18as well as the layout and the hero image and all of these things.
00:10:22So here's the prompt I gave it. It talked about using a carousel skill,
00:10:25which is actually an irrelevant skill for this.
00:10:26It has nothing to do with Higgsfield MCP. So I said, ignore that skill.
00:10:30Let's start with the cover slide.
00:10:31So that main slide that everyone's going to see I want it done in the same styles
00:10:35of reference image. I gave you use your copy, use Higgsfield MCP,
00:10:39use GPT images too for variations. And that's kind of wordy,
00:10:43which is totally why you would eventually want to turn this into an actual skill.
00:10:47This is something you're doing a lot.
00:10:48So remember we are trying to create something that looks like this because we are
00:10:53feeding this exact reference image in there.
00:10:55And we're saying do something similar, just change up the copy,
00:10:58change up the title. So it just came back with the four variants.
00:11:01It took about five minutes.
00:11:03Understand the speed at which this is going to happen is going to be totally
00:11:07dependent on the model you use and the quality. So for GPT image two,
00:11:12I was doing high quality two K and I wanted four variations.
00:11:15Another thing you need to think about when you do this is the way the MCP works is
00:11:19you are just sending a request.
00:11:21It's not going to hit you back up when it's done. So you need to tell cloud code,
00:11:25Hey,
00:11:25I want you to pull Higgsfield every 60 seconds,
00:11:2890 seconds to see if it's done and then bring it back to me.
00:11:32So here's the four variations. We got one, two, three, four.
00:11:37Now we pretty much told it do the exact same thing.
00:11:39Just put our new copy on it. And it did exactly that. Actually,
00:11:43I think it looks pretty good. If I wanted to edit something,
00:11:46I'd probably get rid of the list down here.
00:11:48And I'm not sure if I am in love with the chase AI up top,
00:11:52but point being, if you tell it, Hey, use this reference image,
00:11:55it's sending the reference image.
00:11:56It's just like if you were in there doing this manually. And so step one,
00:12:00giving it some sort of reference image that uses a cover, did a solid job.
00:12:04Now let's see how well it does here in terms of these body slides.
00:12:07Now you'll notice here,
00:12:08we're actually grabbing some stuff from the GitHub page itself.
00:12:12So what I'm going to tell cloud code to do here is find your own assets
00:12:17that we could use as reference images that would make sense for the value page.
00:12:21Again,
00:12:21like we have the full power of cloud code here to help improve the quality of the
00:12:26work. So I said, first slide looks good. Let's move into the body slide.
00:12:30Use the first GitHub repo that's up. And then I said, Hey,
00:12:34go ahead and figure out what assets we need from the GitHub itself to be used in
00:12:39this generation. Research that GitHub poll assets as needed,
00:12:42add them to the MCP request as well. So I'm having it do quite a bit here,
00:12:46like going on the internet, find the appropriate repo,
00:12:48grab what you need from there, bring it into your prompt,
00:12:51and then push it to the MCP. And here's what it came back with.
00:12:54And it was giving us the slide for awesome design dot MD for reference.
00:12:58This is what the awesome design that MD GitHub looks like. So
00:13:02pretty close. I think that looks good. And he gave us four variations,
00:13:08all that are slightly different, nothing really popping out,
00:13:12but I think it did a really good job.
00:13:14It also definitely matches the aesthetic that we gave it here in the reference
00:13:19image. So really, really good.
00:13:21And now all we would do is repeat that exact same process for the rest of the
00:13:25slides. And we wouldn't have to go one by one at this point.
00:13:27We could essentially have it rapid fire all of them.
00:13:30And so you could see how easily we can turn something like this into a content
00:13:35machine,
00:13:35especially if we have some resource like daily updated
00:13:41GitHub repo list, this is just like an evergreen content thing that people would
00:13:45be interested in. And then I can do it all from here.
00:13:47I can turn this into one single skill with the MCP server,
00:13:51really powering the creative side. Now,
00:13:53the one thing I will also mention is you don't have to go full
00:13:57AI image generation for all these things.
00:14:00Like you could also do sort of a hybrid style where we use Higgsfield for the
00:14:04cover image,
00:14:05because this is where the aesthetics is really going to play a big role.
00:14:08And then maybe instead you want to go lower costs, lower tokens.
00:14:12And for the sort of body slides you use like HTML or something,
00:14:16you have cloud code, essentially generate that via code lots of ways to approach
00:14:20this. But the big thing is, is we have, we have options now.
00:14:22We have options now with this MCP server.
00:14:24So that's where I'm going to leave you guys for today.
00:14:27All the links for this stuff can be found inside the description.
00:14:30Make sure to check out chase AI.
00:14:32Plus if you want to get your hands on my cloud code masterclass.
00:14:35And as always, I will see you around.

Key Takeaway

Integrating the Higgsfield MCP server with Claude Code enables the complete automation of high-traffic content creation by programmatically linking trending data research with over 30 top-tier AI image and video models.

Highlights

  • Higgsfield MCP connects 17 image models and 14 video models to Claude Code through a single programmatic pathway.

  • Automated workflows generated 100,000 views in under 24 hours by leveraging trending GitHub repository data.

  • The system eliminates the need for individual API integrations, payment management, and manual tool switching as market leaders change.

  • Claude Code can autonomously research GitHub, extract technical assets, and generate formatted social media carousels via natural language commands.

  • Integration is possible through both the claud.ai web interface and the local terminal using the Higgsfield connector string.

Timeline

Centralized AI Content Tool Integration

  • Higgsfield MCP provides a single entry point for various AI content creation tools.
  • Individual API connections are inefficient because the highest-performing models change frequently.
  • The server offers access to 17 image models, 14 video models, and proprietary Higgsfield tools.

Connecting individual tools to Claude Code is historically difficult due to fragmented APIs and varied payment structures. Users often remain locked into outdated models like Nano Banana Pro or Veo while newer tools like GPT Images 2 or Seed Dance take the lead. This MCP server solves the bottleneck by allowing a single connection to manage the entire ecosystem of generative media tools.

Automating Research and Creation Workflows

  • Claude Code can monitor GitHub repositories daily to identify trending AI projects.
  • Data from repositories is automatically structured into social media carousel copy.
  • Images are generated by Higgsfield based on specific repository information without manual intervention.

The transition from convenience to automation is the primary benefit of this setup. Claude Code acts as an agent that pulls real-time data, analyzes it, and sends prompts to Higgsfield to create deliverables. This process turns a coding assistant into a marketing engine capable of producing relevant content from external data sources like GitHub or Obsidian.

Installation and Web App Configuration

  • The web application setup requires adding a custom connector in the claud.ai settings.
  • Images generate inline within the chat interface, allowing for immediate visual review.
  • Interactive options permit users to recreate, animate, or edit generated images using reference links.

Setting up the connector involves copying a specific Higgsfield string into the Claude web app settings. Once connected, users can prompt the model to use specific tools like GPT Image 2 or Nano Banana 2 directly in the chat. The interface supports iterative editing where the AI maintains the reference image while applying new prompt instructions.

Terminal Setup and Advanced Automation Skills

  • Claude Code terminal setup uses the '/mcp' command to verify active server connections.
  • Automation scripts can be configured to pull Higgsfield status every 60 to 90 seconds to check for completed renders.
  • Natural language commands replace complex scripting for batch image generation.

Setting up the server in the terminal requires a simple natural language request for Claude Code to configure the Higgsfield MCP. While the terminal cannot display images directly, it excels at executing batch processes, such as generating 16 variations at once. Because the MCP operates as a request-based system, the user must instruct the agent to poll the server periodically to retrieve finished assets.

End-to-End Content Generation Case Study

  • Automations can rank the top 10 trending GitHub repos from the last seven days based on star counts.
  • Reference images allow the AI to match existing brand aesthetics and slide layouts.
  • Hybrid workflows use AI for high-impact covers and HTML/CSS code for lower-cost body slides.

A practical application involves Claude Code researching a specific repository like 'awesome-design.md', pulling its assets, and generating a themed carousel slide that matches a provided aesthetic. This demonstrates the AI's ability to browse the internet, find relevant visual assets, and incorporate them into a prompt for the Higgsfield server. Users can optimize token usage by reserving AI generation for complex cover images while using code-based generation for text-heavy slides.

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