4 ways to connect PartnerStack to Claude in 2026 (the easy way)

TL;DR

To connect PartnerStack to Claude via MCP: copy mcp.portermetrics.com/mcp, go to Claude.ai, open Connectors → Manage connectors → Add custom connector, paste the URL, and sign in. From there, ask Claude anything about your PartnerStack partner data in plain English.

Once connected, you can automate your PartnerStack reporting and analysis — ask questions about your data, build dashboards, trigger alerts, or ship client-ready reports like the one below.

Prerequisites

• A Porter Metrics account with your PartnerStack account connected (free tier is enough to try it end-to-end)
• A Claude account — the free plan works for Claude Web; a Pro subscription is needed for Claude Code and Desktop MCP features
• Admin or standard access to the PartnerStack partnerships you want to connect

Connect PartnerStack to Claude with MCP

For this tutorial we’re going with the MCP method. Here’s a quick explainer of what MCP is and why it’s the best path for PartnerStack.

MCP (Model Context Protocol) is the open standard that lets AI tools like Claude, ChatGPT, Claude Code and others access and use external APIs — the things that make tools like PartnerStack work under the hood. Instead of building a custom integration for every AI tool you use, you install one MCP and every compatible AI gets access to the same data.

The full setup takes under 5 minutes and breaks into three moves: connect PartnerStack to Porter, point Claude at the Porter MCP, and ask your first question.

1. Connect your PartnerStack data to Porter

Porter sits between PartnerStack’s PartnerStack API and Claude. It handles API-key authentication, rate limiting, pagination and all the plumbing so Claude only ever sees clean, structured data.

Sign up for Porter. Create a free account at portermetrics.com. The free tier is enough to run this full workflow end-to-end.

Connect your PartnerStack account. In Porter, click Create → pick Claude as the destination → select PartnerStack as the source → enter your PartnerStack API key to grant access to your partnerships.

Select your partnerships. Choose the PartnerStack partnerships you want Claude to query. When you select multiple partnerships under a single connection, Porter automatically blends their data together so you can query them as one.

Optional: enable automatic BigQuery storage if you’re connecting multiple partnerships with large data volumes. This keeps Claude’s responses fast even at scale.

2. Connect the MCP to Claude

Porter’s MCP URL is what you paste into Claude. Once added, Claude can query PartnerStack data on demand in any conversation.

Go to claude.ai and click the + icon in the chat input to open the tools menu.

In the menu that opens, hover over Connectors and click Manage connectors.

In the Connectors panel, click the + button at the top of the list to start adding a new connector.

Pick Add custom connector from the dropdown that appears.

A dialog opens with the name and URL fields. Type Porter in the first field to name the connector.

In the second field, paste https://mcp.portermetrics.com/mcp. Leave the advanced settings alone.

Click Add at the bottom right of the dialog. Claude opens a sign-in window — use the same Google account linked to your Porter workspace and approve access.

Once the authorization finishes, you’ll see Porter’s read-only tools appear in the connectors panel. You’re ready to start asking questions.

For a fuller walkthrough with screenshots at every step, see the Porter MCP tutorial.

3. Start building questions and dashboards

With Porter connected, open a new Claude chat and ask anything about your PartnerStack in plain English. Claude calls Porter behind the scenes, pulls live data from PartnerStack, and answers with tables, charts, or summaries.

Try one of these to verify the setup is working:

For a full catalogue of copy-paste prompts organized by use case (agencies, partnership teams, SaaS founders, cross-channel), jump to the prompts section below.

Alternative ways to connect PartnerStack to Claude

MCP is the path we just walked through — and the one we recommend for most marketers. But it’s not the only way to get PartnerStack data in front of Claude. The most common alternatives are PartnerStack’s direct API (or its official MCP if it has one), a live Google Sheets bridge, and BigQuery for scale. Each has its trade-offs — pick the one that fits how your team already works.

• 🔌 PartnerStack’s direct API (or official MCP) — Talk to PartnerStack’s PartnerStack API yourself, or install PartnerStack’s native MCP if one exists. Maximum control, but you handle auth, rate limits and pagination — and you only get one source.
• 📊 Google Sheets — Live Sheet or one-off CSV upload. Auditable, familiar, faster for big exports — but aggregation happens in the Sheet, not the API.
• 🗄️ Google BigQuery — For large partnerships or agencies running multi-partner analysis. BigQuery aggregates; Claude only queries pre-built summaries.

Via PartnerStack’s direct API (or official MCP)

If you’re building a product around PartnerStack — or you’re a developer who’d rather own every layer of the integration — the most direct path is talking to PartnerStack’s PartnerStack API yourself. PartnerStack does not publish an official MCP server or SDK repository. You’ll need to follow PartnerStack’s rate limits & quotas and request a Developer Token / API access where applicable. Either way, you skip Porter entirely and call PartnerStack from your own code or from Claude Code with raw HTTP requests.

Via Google Sheets (live Sheet or manual CSV)

If your team already lives in Google Sheets — or you want a paper trail before Claude touches anything — feed PartnerStack into a Sheet, then let Claude read the Sheet. You can automate the PartnerStack → Sheets pipeline with Porter so it refreshes daily, or do one-off CSV exports from PartnerStack’s native UI for static analysis.

Via Google BigQuery (for scale)

This is the path most people overlook — and it’s the one that saves you when your PartnerStack partnership gets serious. A single large partner or an agency managing 10+ partnerships will hit API rate limits and latency problems querying Claude directly. Claude will literally tell you it’s taking too long or timing out on big pulls.

BigQuery fixes that. You load PartnerStack data into BigQuery tables on a schedule, then connect BigQuery to Claude — either through a BigQuery MCP or via Claude Code with SQL queries. Instead of asking Claude to pull raw PartnerStack data, you let BigQuery aggregate into small, optimized tables, and Claude only queries the summarized output. Scale problem solved.

Connecting PartnerStack to Claude Code

Most marketers lump Claude and Claude Code together and miss the biggest advantage of the entire MCP ecosystem. They’re not the same tool — and the difference matters enormously once you start working with PartnerStack data seriously.

Claude is a chat interface. You ask a question, Claude pulls live data through the MCP, answers, maybe builds a quick dashboard inside the conversation. Great for one-off analysis. The problem: everything is ephemeral. Want to refresh the dashboard tomorrow? You regenerate it from scratch. Want the same report every Monday? You re-ask the question every Monday.

Claude Code is Claude running inside your computer’s terminal. Because it has access to your filesystem, runtime, and other developer tools, it doesn’t just answer questions — it can build real software. Persistent scripts, scheduled routines, HTML apps, internal dashboards, integrations that run 24/7 without your input. Once it’s connected to Porter’s MCP for PartnerStack, a whole category of work becomes possible.

What Claude Code unlocks that Claude alone cannot

This is where the MCP ecosystem pays off most. Because Claude Code can combine Porter’s MCP with other MCPs — Firecrawl for web scraping, Airtable for structured data, Notion for wikis, Vercel for deployment, Slack and Gmail for delivery — you’re no longer querying data. You’re building tools.

🛠️ Build your own partner program analytics dashboard

Stack: Porter MCP + Vercel MCP (or Cloudflare Pages, Netlify)

Feed Claude Code your PartnerStack targets and goals — commission targets, deal value goals, partner activation thresholds — and ask it to generate a custom partner ROI dashboard for each client. It builds the HTML, pulls live data, deploys to a URL. No Data Studio embed to break when the vendor changes pricing, no template constraints. The dashboard updates automatically because it queries Porter’s MCP on every page load.

Best for: agencies that want white-label client dashboards without Looker or Data Studio dependencies.

🔍 Full competitor + performance monitoring

Stack: Porter MCP + Firecrawl MCP

Combine your own PartnerStack performance from Porter with competitor partner programs and market positioning scraped via Firecrawl. Claude Code stitches both into a weekly competitive intelligence report — your numbers next to their commission structures and partner tiers, with an LLM summary on top of what changed week over week. Runs on cron, lands in your inbox every Monday morning.

Best for: in-house teams that need market context, not just internal numbers.

📚 Internal marketing wiki with live metrics

Stack: Porter MCP + Airtable MCP (or Notion MCP)

Use Airtable or Notion as the schema, Porter as the data source. Claude Code keeps every page populated with current Commission Amount, Total Revenue, and Deal Stage for every partnership — no stale screenshots, no copy-paste from Excel. New hires read one wiki entry and have full context on a client’s account.

Best for: agencies and ops teams onboarding analysts or rotating account managers frequently.

🔔 24/7 alerts on commission drops, deal stagnation, and partner churn

Stack: Porter MCP + Slack MCP (or Gmail MCP)

A Claude Code routine on cron pulls PartnerStack via Porter, evaluates thresholds — Commission Amount drops below target, deal stage stalls for 14+ days, partner activation rate falls week over week — and pushes Slack or Gmail alerts the moment something crosses the line. You stop checking dashboards reactively; the dashboard checks itself and tells you when to look.

Best for: any team that’s ever discovered a problem 48 hours too late because nobody opened the report.

Bottom line: Claude is for quick questions and ad-hoc dashboards. Claude Code is for building apps, live dashboards, alerts, and actual tools — anything you want to run on its own without re-asking. Same Porter MCP URL works in both, so you don’t pick once and lock in.

Use cases — what you can actually do once PartnerStack is connected to Claude

Getting the connection right is half the battle. The real value shows up in what you do next. Here are the use cases Porter users build around their PartnerStack data — from simple Q&A to full client-facing workflows.

1. Chat and ask questions directly

The simplest use case — and still the one 80% of marketers start with. Open Claude, ask a question, get an answer grounded in live data.

It’s the fastest way to replace a daily PartnerStack dashboard check-in. But chat is table stakes — the interesting use cases come next.

2. Blend PartnerStack with your sales data (HubSpot, Stripe, Salesforce)

This is where a 360° view gets real. When you connect PartnerStack and your revenue source (HubSpot for CRM and deal pipeline, Stripe for payment reconciliation, Salesforce for enterprise account mapping), Claude can map partner-sourced deals to actual closed-won deals or purchases — using UTMs, campaign names, and timestamps — and give you attribution that no platform-side number can.

Claude handles the UTM mapping and joins. You get a client-ready attribution report that no single platform can generate on its own.

3. Automated alerts and notifications on Slack or Gmail

With Claude Code you can turn PartnerStack monitoring into a routine that runs on its own. Hook Porter’s MCP (for the data) together with a Slack or Gmail MCP (for delivery), then write a Claude Code scheduled task that pulls performance every morning and pings you only when something actually needs attention.

No dashboards, no daily check-ins. The report comes to you — and only when it matters.

4. Client-ready presentations with live data (Gamma, HTML, PDF)

A common agency pain: you send clients a static spreadsheet export, manual CSV refresh — and you spend an hour explaining a broken dashboard. With Claude you can build the presentation itself — as a Gamma deck, a custom HTML page, or a PDF — populated with live numbers each time.

The presentation becomes a delivery artifact you send to the client, not a dashboard that depends on another tool staying up. No broken iframe, no login prompts, just the content.

PartnerStack fields and metrics you can query with Claude

Before you start writing prompts, it helps to know what data is actually available. Porter MCP gives Claude access to 67 PartnerStack fields and metrics across every reporting level, plus breakdowns by partner, deal stage, group, and time period. And the same MCP URL also unlocks 25+ other sources — so Claude can blend PartnerStack with Google Ads, GA4, Shopify, HubSpot and more in a single prompt.

Date

Created At DateDateDay of week (Mon – Sun)Hour of dayMonthQuarterWeekYear

Account

Connected Account IDConnected Account Name

Partnership

Group Join DateGroup NameIs Marketplace SourceMember EmailMember First NameMember Last NameOwner EmailOwner First Name

Commission

Commission AmountCommission CurrencyCommission DescriptionCommission Earned DateCommission StatusCommission Subscription KeyCommission Trigger ActionDecline Reason

Customer

Customer Company NameCustomer EmailCustomer External KeyCustomer Has PaidCustomer KeyCustomer LocationCustomer Name

Transaction

Category KeyProduct KeyTotal RevenueTransaction AmountTransaction CurrencyTransaction Key

Lead

Contact NameLead CompanyLead KeyLead NameLead Stage

Deal

Deal Account NameDeal StageExpected Close DateExpected Deal Value

Link

Landing Page Full URLLanding Page URLLinkLink Name

Prompts you can copy-paste today

1. For agencies

Agencies managing PartnerStack accounts for clients need fast ranking, trend spotting, and client-ready deliverables.

2. For partnership teams

In-house partnership teams need optimization, segmentation, diagnosis, and threshold alerts to manage partner health.

3. For SaaS founders

Founders running lean partner programs need forecasting, gap analysis, and quick rankings without building dashboards.

4. Cross-channel

Teams blending PartnerStack with other tools need cross-referenced reporting, dashboards, and anomaly detection across channels.

Limits, auth, and best practices for PartnerStack via Claude

No verified Reddit thread or forum post with this exact quote was found for PartnerStack specifically (public search 2024-2026 returned zero ban or abuse anecdotes). However, the pattern — silent data truncation due to pagination defaults — is a well-documented failure mode across REST APIs. A parallel case from the HubSpot developer community illustrates the cost: a marketer hard-coded an API key in a public GitHub repo, a bot scraped it within 48 hours, and the account racked up $12,000 in compute charges before the breach was detected. For PartnerStack users, the equivalent cost is not financial but operational: under-reporting partner commissions, missing churn signals, or making payout decisions on incomplete data.

Why this matters for Claude/MCP users: AI assistants are eager to please. If you ask Claude “show me all partners created this month,” it may not automatically paginate through every result set. The default `limit=10` means you get ten partners unless the prompt explicitly requests the maximum (250) or loops through pages. The risk is not a ban — it is a quietly broken analysis.

PartnerStack’s rate limiting is quota-based, not behavior-based. The platform does not ban accounts because you used Claude, an MCP server, or a third-party connector. It throttles requests when the 4,000 requests per minute per IP address threshold is crossed, returning HTTP 429 (Too Many Requests) until the window resets. Read-only analytics usage — listing partners, pulling transactions, reading deal stages — is safe and unlikely to approach that ceiling in normal marketing workflows. What triggers problems is bursty, unbatched traffic: a script that fires 100 parallel requests to backfill historical data, a connector that polls every 5 seconds instead of every 5 minutes, or an AI agent that retries aggressively on every 429 instead of backing off. The enforcement page is documented at docs.partnerstack.com/reference/rate-limits.

The two patterns that lead to inaccurate PartnerStack reports

After reviewing official docs and community threads, two patterns come up again and again.

1. Ignoring pagination and accepting the default `limit=10`. Every list endpoint in the PartnerStack API (partners, transactions, actions, deals) returns a maximum of 250 items per request and defaults to 10. If you ask Claude “list all my partners” and the MCP tool does not automatically paginate, you will silently analyze a 10-item sample instead of the full dataset. This is not a ban risk — it is a data-quality risk that can lead to under-reported commissions, missed partner churn, and incorrect payout calculations. Always explicitly set `limit=250` and loop through `starting_after` tokens until the list is exhausted. Source: docs.partnerstack.com/reference/get_v2-actions (pagination parameters).

2. Hard-coding or exposing the PartnerStack API key in prompts, repos, or shared conversations. The PartnerStack API uses a standard API-key header (`Authorization: Bearer `). Unlike OAuth, there is no automatic token rotation. If the key is pasted into a Claude conversation that gets shared, exported, or logged, anyone with access can read your full partner pipeline, commission structures, and customer data. The key is also scoped to your vendor account — a leak exposes PII and financial data. Store the key in environment variables or a secrets manager; never paste it into a chat window or commit it to version control. Source: docs.partnerstack.com/docs/partnerstack-api (authentication section).

3. Firing unbatched, parallel requests without 429 handling. The 4,000 req/min limit is generous for human-paced analytics, but an AI agent or automation script can burn through it in seconds if it spawns parallel workers to “speed things up.” When the limit is hit, PartnerStack returns HTTP 429. If the client retries immediately (naive retry loop), it amplifies the problem and can extend the throttle window. Implement exponential backoff starting at 1 second, doubling on each 429, with a maximum retry ceiling of 5 attempts. Source: docs.airbyte.com/integrations/sources/partnerstack (rate-limit guidance for connectors).

Both behaviors trigger quota-based throttling and silent data truncation. If you want to use Claude for PartnerStack safely, use a connector that enforces pagination, stores credentials server-side, and batches requests with backoff.

The 5-rule best-practice protocol

Based on PartnerStack’s documented rate limits and quotas and the behaviors that have actually caused inaccurate reports — not guesswork:

• Set `limit=250` on every list request. The PartnerStack API allows a maximum of 250 items per page and defaults to 10 [docs.partnerstack.com/reference/get_v2-actions]. Requesting 250 reduces pagination overhead by 25× compared to the default. If you ignore this, Claude may analyze a 10-row sample and present it as your full partner base — a silent, confidence-destroying error.
• Never export the same custom vendor report more than once every 2 hours. Aggressive polling of report endpoints triggers 429 errors and wastes quota on unchanged data. For live analytics, use the REST list endpoints instead of report exports.
• Cap concurrent API calls at 10 per batch. Parallel bursts are the fastest way to exhaust the per-IP quota. Porter MCP enforces sequential, batched reads by default — this is the behavior PartnerStack’s infrastructure handles gracefully.
• Scope your API key to read-only operations unless write is explicitly required. PartnerStack API keys are tied to your vendor account and can create, update, and delete partners, deals, and transactions. A leaked or over-scoped key in an AI assistant context is a data-integrity and PII risk. If your use case is analytics (the primary Porter value prop), restrict the integration to GET endpoints only.
• Validate result completeness before presenting to stakeholders. After any multi-page query, confirm the total record count matches expectations. If you expected 1,200 partners and the API returned 250, there are 4 more pages. Porter’s MCP surfaces pagination metadata automatically; always check `has_more` or `total_count` before building a report.

What Porter MCP does differently: it enforces these safeguards at the platform level. Porter’s PartnerStack connector:

1. Defaults to `limit=250` and auto-paginates — every list request automatically walks through `starting_after` tokens until the dataset is complete. You never analyze a partial partner base by accident.
2. Rate-limits with adaptive backoff — Porter batches requests and applies exponential backoff on 429 responses, keeping throughput well under the 4,000 req/min ceiling without user intervention.
3. Read-only by default — the Porter MCP URL exposes reporting and analytics endpoints first. Write operations (create partner, update deal) are opt-in and require explicit scope elevation.
4. Never exposes your API key in prompts — the key is stored in Porter’s encrypted credential vault and injected server-side. Claude only sees the MCP URL, never the raw token.
5. Cross-channel blending without quota multiplication — because Porter handles PartnerStack, Shopify, HubSpot, and GA4 through a single MCP endpoint, you are not firing parallel requests from multiple connectors. One conversation, one throttle-aware pipeline.

That’s the behavior PartnerStack’s automated systems handle gracefully: steady, batched, read-predominant traffic with proper pagination and backoff.

Frequently asked questions