6 Ways GTM Teams are using MCPs and Claude/Claude Code Today

+ Video demos of real examples from yesterday's live event

Apr 15, 2026

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Hey, y’all! 👋

A couple of weeks ago, I put out a post on LinkedIn that basically said: I want to gather some GTM operators to share examples of AI agents/workflows they’ve built using MCPs and Claude/Claude Code. There was a lot of interest, so I put together a live event. Close to 800 people registered. And almost 400 builders joined live (wylin). Six people presented what they’ve built using MCPs inside Claude (across chat, cowork, and code).

And what I saw across all six presentations was a pattern I think most GTM teams are going to feel very soon: the gap between teams that have connected their tools to AI and teams that haven’t is getting wider every week.

MCPs are the connective tissue. They let Claude talk to your CRM, your data warehouse, your call recorder, your prospecting tools. Without them, Claude is smart but blind. With them, it becomes something closer to a second brain that actually knows your business.

Claude is powerful.
GTM tools are powerful.
But when you combine the two, it’s a real “1+1=10” situation.

I had a blast seeing all the creative ways GTM teams are using MCPs to build inside of Claude. It’s a pleasure to learn alongside everyone at this particular moment in time.

I hope this post inspires you to build something for your team. Even if that means you don’t read this entire article. That’d be a win in my book. :)

Each presentation has a video on YouTube (smash that subscribe button on The Signal’s YouTube channel while you’re there 😂).

Here are the use cases:

How an SMB AE at Zendesk Uses Sumble + Claude Cowork to Build Micro-Campaigns (Eric Fitz, AE at Zendesk)
How Nooks’ RevOps Team Created a Company Brain with Claude Code by Building a DIY Data Warehouse and a custom MCP to query data from HubSpot, Mixpanel, Nooks, and more (Josh Nelson, RevOps/GTM Engineer at Nooks)
How Clarify’s MCP Turns Claude into a Full-Cycle Sales Assistant (Vinayak Mehta, Software Engineer at Clarify)
How Attention Auto-Clusters Churning Prospects and Triggers Targeted Outreach with Claude Code (Anis Bennaceur, Co-founder & CEO at Attention)
How WorkSpan Built a Headless Context Layer so Reps Get Everything They Need Directly in Claude Chat (Sam Gong, SVP Marketing at Workspan)
How 25 Agents in Slack Run MintMCP’s GTM Org, Built with Claude Code and Custom MCPs (Jiquan Huang, Co-founder & CEO at MintMCP)

Alright, let’s get into it.

1. How an SMB AE at Zendesk Uses Sumble + Claude Cowork to Build Micro-Campaigns

(Eric Fitz, AE at Zendesk)

Eric is an SMB AE at Zendesk. He has 1,800 accounts. Every rep who’s had his territory for the past five years has had the same data. He needed an edge and found it in Sumble.

Eric’s workflow starts in Claude Cowork with the Sumble MCP connected. He uses Cowork (not chat) because it needs a bigger context window. He uploads a CSV of his full account list, then asks Claude to do something very specific: identify which of his 1,800 accounts have call center technology.

Why call centers? Because at Zendesk, if a company runs a call center, they auto-qualify for every other type of support Zendesk offers. It’s the biggest deal type. And until Sumble, there was no tool that could reliably surface call center usage.

Sumble pulls this from a combination of HTML stack detection, job posting data, and historical hiring signals. Out of Eric’s 1,800 accounts, it found 61 companies with call center tags.

From there, Claude finds director-level+ contacts at those companies using Sumble’s MCP, then drafts personalized emails for each one. Eric showed a draft where Claude referenced the prospect’s exact tech stack (TalkDesk, Five9, Genesis) in the subject line. Nobody else is sending that subject line.

Eric also built what he calls the “Zendesk Sales Brain,” a Claude Skill where he pointed Claude at Zendesk’s public-facing Help Center and had it learn every major sales play, competitive positioning angle, and product capability. That context now feeds every prompt.

Eric works in batches of ~30 contacts per micro-campaign. At a 4% reply rate, that’s one reply per batch. If he runs 30 batches, that’s 900 contacts and 30 potential conversations. He drafts them in Claude Cowork, which pushes them into Gmail drafts, and then he sends them from his phone.

Eric said he’s sent roughly 5,000 emails using Sumble-sourced data, and only 10 have bounced. That’s a 99.8% deliverability rate.

He showed an example where the workflow surfaced a contact whose LinkedIn title was “Implementation Manager” but whose actual job description (pulled by Sumble) showed she runs the entire call center. Eric explained how he never would have found her searching by title alone. The MCP is what makes Sumble’s data layer actionable inside Claude rather than living in a separate tab.

Try Sumble (it’s free) at Sumble.com. Then set up their MCP inside Claude. Have fun. :)
Connect with Eric on LinkedIn.

2. How Nooks’ RevOps Team Created a Company Brain with Claude Code by Building a DIY Data Warehouse and a custom MCP to query data from HubSpot, Mixpanel, Nooks, and mor

(Josh Nelson, RevOps/GTM Engineer at Nooks)

Josh is a GTM Systems Engineer at Nooks. Small RevOps team. And like most small ops teams, he was drowning in ad hoc data requests. Finance needs a list of CSMs for every invoice. Leadership wants to know how many calls an SDR made that turned into closed-won deals. Everybody wants a report, and the data lives in five different systems. Classic.

So, naturally, Josh decided to build a data warehouse from scratch 🤪. During a hackathon, using Claude Code.

He set up a Postgres database on Render (free tier to start, a couple hundred bucks a month once the data volume grew), then built ETL pipelines that pull data from HubSpot, Mixpanel, Nooks’ own product database, ChiliPiper, and their CPQ tool (Subskribe). The ETLs run every few hours via cron jobs, keeping the database current.

On top of that, he deployed Redash, an open-source SQL query tool. Redash sits on top of the Postgres database and lets anyone write queries against it. And every query in Redash has its own API key, which means Claude Code can call Redash, run a query, and return the result.

Josh showed the progression: drag two CSVs into Claude, ask it to match CSM names to invoices. Two minutes, done. Then he realized if Finance needs that report regularly, why is he still manually pulling CSVs? So he automated the entire thing.

Then Josh built a custom MCP server (also hosted on Render) so his teammates could access the database through Claude’s UI. No terminal. No IDE. Just open Claude, connect to the MCP, and type “How many calls did Noah make this quarter that ended in closed-won deals?” Claude queries Redash, joins the tables, and returns the answer. Within 10 minutes, he had a working MCP with OAuth, and his teammate was querying the database in plain English.

He also figured out how to embed Redash reports into HubSpot dashboards (via iframe). So leadership gets their reports exactly where they already look, and Josh can build custom HubSpot reports that wouldn’t be possible to do natively.

The whole thing would have taken a year and a database administrator two years ago. Josh built it during a hackathon weekend with Claude Code.

(By the way, Nooks is productizing a version of this in their new sales engagement platform. I think they should name it JoshBrain.)

Check out Nooks.
Read more details (including costs) here: How I built an interactive company brain for our RevOps team.
Connect with Josh on LinkedIn.

3. How Clarify’s MCP Turns Claude into a Full-Cycle Sales Assistant

(Vinayak Mehta, Software Engineer at Clarify)

Vinayak is a software engineer at Clarify. Clarify is building an autonomous CRM, and they launched their MCP server a few months ago. Vinayak walked through four use cases, all inside Claude chat.

1. Updating a CRM from text message screenshots.

A couple of Clarify’s customers chat with prospects on iMessage. Instead of going back to Clarify to manually enter deal details, they just paste a screenshot of the text conversation into Claude. Claude reads the image, extracts the relevant info, and uses Clarify’s MCP to create the company, person, and deal object automatically.

2. Prospecting and campaign building.

Vinayak typed a natural language query: “Find me VPs of Sales in New York who work at Fortune 500 companies.” Claude used Clarify’s lead finder, found around 2,000 results, imported 10 for the demo, and created a list in Clarify with full contact details including emails. Then he asked Claude to build a 3-step email campaign for those contacts. Claude drafted the campaign, and because Clarify syncs all your sent emails, it matched the rep’s actual writing style.

3. LinkedIn ad retargeting workflow.

This one came from Chris Eberhardt on Clarify’s marketing team. They run LinkedIn thought leadership ads, and every week LinkedIn sends a list of which companies are engaging most. Chris built a Claude skill that takes that list, sends it into Clarify’s lead finder via MCP, finds people (specifically, their personas: founders and heads of sales) at those companies, and enrolls them in an outbound campaign. Automated intent-to-outreach pipeline. This is a killer ABM play.

4. Parsing contracts to update deals.

After closing a deal, a rep pastes the contract PDF into Claude. Claude reads it, extracts the deal amount, contract start date, signer name, job title, and uses Clarify’s MCP to update all the deal fields automatically. No manual data entry.

Across all four, the pattern is the same: Claude does the thinking, Clarify does the CRM work, and the rep never has to leave the Claude chat window.

Try Clarify (it’s free) at Clarify.ai. Add the MCP via Claude’s connectors (Settings → Connectors → Browse → search “Clarify”).
Connect with Vinayak on LinkedIn

4. How Attention Auto-Clusters Churning Prospects and Triggers Targeted Outreach with Claude Code

(Anis Bennaceur, Co-founder & CEO at Attention)

Anis is the Co-founder & CEO of Attention. His thesis: the best outbound intelligence is already sitting in your call recordings (and emails/slacks). Most teams just don’t know how to extract it.

He showed a workflow that starts with one prompt in Claude Code: search Attention’s MCP for discovery, demo, and intro calls from the past 90 days. From those conversations, Claude extracts why prospects are evaluating Attention, what tools they’re coming from, what their compelling events are, and what specific language they use to describe their pain (“your best messaging is not written, it’s found”).

That output gets pushed to internal markdown files (Anis calls it his “second brain”), which Claude then uses to build prospect clusters. Not basic ICP segments. Actual persona pockets with names Claude generated on its own (so cool to see!).

He showed two of them.

Pocket 1: “The RevOps Builder at a Scaling SaaS Company.”
Pocket 2: “The Frustrated Veteran.”

These aren’t arbitrary labels. Each pocket includes real examples from past calls, the exact pains those people described, the value prop that resonated, targeting criteria (role, seniority, headcount range) and recommended messaging.

Claude then pushes these clusters into Clay’s connector to enrich the data further: firmographics, growth signals, years of experience, company characteristics. The result is a set of 5–10 hyper-targeted persona pockets, each with its own outreach playbook.

The workflow runs on a schedule. Every day, Claude Code pulls the latest calls from Attention’s MCP, looks for new patterns, updates the persona clusters, and sends a Slack notification with any new learnings or outbound ideas. Anis reviews them in the morning and shares the best ones with the team.

He shared this is how you run outbound experiments that get a K-factor above 1. Every new customer conversation generates new intelligence. A fraction of that intelligence produces a new insight. That insight creates a new micro-campaign. That campaign generates new customers, who generate more conversations.

Attention’s MCP isn’t just a raw transcript dump. Their “superagent” batches analysis across 25 calls in parallel, structures the output for LLMs, and surfaces patterns that a basic call recorder MCP can’t. The quality of the MCP matters just as much as having one. They’re truly building an AI-native GTM product.

Learn more about Attention at Attention.com.
Connect with Anis on LinkedIn.

5. How WorkSpan Built a Headless Context Layer so Reps Get Everything They Need Directly in Claude Chat

(Sam Gong, SVP Marketing at Workspan)

WorkSpan is a complex product with a complex buyer. Sam’s team had been experimenting with AI for two years: N8N flows, Clay tables, Claude projects, OpenAI agents. Every deployment required loading context. And that context went stale almost immediately.

Sam looked at Octave (a context layer tool) a year ago and passed. It was a dictionary of objects: products, segments, personas, use cases, competitors, proof points. Filling it out would take weeks of manual work.

Then Octave added an MCP.

Sam got on a one-hour call with Octave’s team, opened Claude Code, uploaded three days of SKO transcripts and workshops, and the MCP auto-populated about 60% of Octave’s objects. The tool went from “I’ll never set this up” to “we can’t live without it” in a single session.

Now Octave is WorkSpan’s shared context layer. Sam updates it from Claude after every product meeting, every positioning change, every new competitive development. One prompt: “Hey Claude, we just released a new integration. Update Octave.” The MCP handles the rest.

Sam showed an account plan for Snowflake that Claude generated using Octave. It identified four potential sales plays, ranked them by relevance, pulled in specific reference customers for each play, and flagged an AWS migration mandate that’s time-sensitive. No rep would have assembled that on their own.

The same Octave context then flows into pipeline reports, rep onboarding, and prospecting flows. Update it once, and it compounds across every AI-powered surface in the org. Sam said it best:

“I would not have bought this tool without the MCP.”

Try Octave (for free) at OctaveHQ.com.
Connect with Sam on LinkedIn.

6. How 25 Agents in Slack Run MintMCP’s GTM Org, Built with Claude Code and Custom MCPs

(Jiquan Huang, Co-founder & CEO at MintMCP)

Jiquan is the Co-founder & CEO of MintMCP. His team is five people. One AE driving seven-figures in pipeline. They’re running a 1-to-5 human-to-agent ratio across the entire business.

His setup was the most advanced architecture anyone showed. Claude Code runs in a sandbox, connects to all their MCP services, uses GitHub as a long-term memory system, and interfaces with the team through both Slack and scheduled tasks that run on a heartbeat (meaning the agents wake up on their own, do work, and report back).

He showed two agents live in Slack:

Charlie is their call analysis bot. After every sales call finishes, Charlie pulls the transcript via MCP, runs it against Gong best practices that JQ loaded in, and posts a coaching breakdown in Slack. Notes, next steps, what went well, what to improve. No one has to go into a dashboard. It just appears in the channel.
Peter is their procurement agent. Peter wakes up every morning and every evening, reviews every active deal, checks every open ticket, and pings the humans (including JQ) with reminders. “Hey, this deal needs attention. Hey, this ticket hasn’t been updated.” JQ joked that sometimes he’s not sure if the AI reports to him or if he reports to the AI.

He also showed a live demo of a Claude workflow that pulls recent LinkedIn (he used me as the example, which was fun), identifies who’s engaging with those posts, cross-references against their ICP, and outputs a Google Sheet of prospects to reach out to. All running through custom MCPs for LinkedIn data, Google Sheets, and their CRM.

Jiquan’s team has 25 agents running across engineering, sales, marketing, and prospecting. They use GitHub as the memory layer, which means agents remember what they did yesterday, last week, and last month. You can train them just by talking to them in Slack: “Do this differently next time.” And they will.

The governance piece is important, too. Jiquan’s company (MintMCP) helps enterprises connect Claude to internal systems with proper auth, permissions, and tool-level access controls. For example, their Attio MCP bundle is read-only. Agents can pull deal data but can’t write to the CRM without human approval. That kind of fine-grained control is what makes the difference between “cool experiment” and “I trust this to run at 6 AM while I’m asleep” at a large company.

Learn more about MintMCP at MintMCP.com.
Connect with Jiquan on LinkedIn.

As I watched the six different presentations, there was one clear pattern to me:

1. Connect your data to Claude (safely) → 2. Let it think → 3. Automate what works → 4. Move on to the next idea.

In six months, every serious GTM tool will have an MCP. The ones that don’t will get left behind. And the reps and ops people who are building AI-native workflows—like these six—are going to start lapping everyone else.

Let me know if we should do more of these types of live events.

As always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Related resources:

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