What Faraday's MCP server is — and what your AI agent can actually do with it

AI agents and propensity modeling are a match made in heaven for a modern marketers toolkit. Your agent can read your data on your own data infrastructure, spot patterns or opportunities, and draft the next move. What it can't do is reach into your connected tools and accounts to access that data itself, check on how a model is performing and provide analytics, or retrain it when it's drifted. When it comes to actually accessing your data and models in Faraday independently, AI agents on their own are relatively helpless. So when it can't take those actions and move the project along, it hits a wall, hands the work back to you, and you go do it by hand in the dashboard.

That's the problem Faraday's MCP server solves. Once connected, our MCP server lets your agents independently pull your real numbers, reason over your models, and even change how they're built — the same things a person can do in Faraday, done in conversation instead of clicks in a dashboard. Whether you're standing up your first model or operating a dozen in production, it's the difference between your agent doing the work and your agent handing it back to you.

What MCP actually is

Model Context Protocol is the emerging standard for letting AI agents connect to external tools directly, instead of a developer building (and managing) a custom integration for each one. Faraday's server is our implementation of this concept.

You connect once, at mcp.faraday.ai/mcp, sign in with your existing Faraday login, and the agent has your whole account within reach: that includes all 1,400+ consumer and identity attributes and your predictive propensity models (including your cohorts and outcomes, your deployed targets, your usage and billing).

It authorizes through OAuth, so the agent never touches an API key — you approve it in the browser, the way you'd approve any other app. Once it's connected, ask it to run whoami and it'll confirm which account it's using and list every tool it has. That's the fastest way to know the connection worked.

What your agent can do with it

The same connection covers a range of jobs. Most brands lean on one or two; agencies use all of them. None of them require you to open the dashboard.

Read your real performance. This is where most teams feel it first. Instead of guessing, your agent can pull the current numbers straight from your account and explain them: how a churn model is doing and what's driving it, a Persona breakdown for your top cohort, a head-to-head on two outcomes, the ROI on an audience since you activated it. It answers from your data, not a hunch — the difference context you can actually verify makes.

Change how your models work. Reading is half of it. Your agent can also build and adjust the things it's reading — define a cohort, stand up an outcome, tune a model's inputs and kick off a rebuild, wire a connection, deploy a target to your ad platform. When the agent spots that a model has drifted, it doesn't just flag it and wait for you. It can act on it.

Reason like a teammate, not an API. The agent can read and update your account's use-case notes — the why behind what you've built, not just the schema. So it treats your account like something it understands, instead of running every request as a cold lookup.

Manage every account you touch. If you run a parent account with a subaccount per client — agencies, resellers, anyone on Powered by Faraday — the agent can list every account it can reach and switch between them without reconnecting. The full build, repeated cleanly across clients instead of by hand each time.

Package repeatable analysis for your clients. If you're an agency or reseller running Faraday across multiple accounts, the same connection lets you standardize the analysis work you're already doing manually for each client. Build a skill that uses the MCP to generate a Persona Set, perform analysis on it, and create a deck of insights and recommendations — the same workflow, run consistently instead of assembled from scratch each time.

Account context: why the agent understands your account

It's something we talk about a lot at Faraday: AI agents need context to operate effectively. Without it, they end up guessing — filling gaps with assumptions, or answering from a generic playbook that has nothing to do with your account.

That's why each Faraday account is paired with an account knowledgebase: a document stored inside your account that describes how your organization uses our platform — your use cases, the reasoning behind each one, the problem (what you're solving), the solution (how Faraday fits in), and other detailed background information (like why you're building with this tool at all). The MCP server reads that knowledgebase directly, so your agent always knows the intent behind your setup instead of just executing API calls blindly. And when things change, the agent can update those use cases too — keeping your account's plan in sync with reality.

With the MCP vs. without it

What you need to do	Sample prompt	With Faraday's MCP	Without it
Check how a model is performing	"How is my repeat-purchase model performing, and what's driving the top scores?"	Agent pulls current metrics and explains the drivers in chat	Log in, open the model, read the analysis yourself
Get a persona or cohort breakdown	"Break down my customer cohort into personas and tell me which traits over-index."	Agent queries it live and summarizes	Navigate to the persona set, export, interpret by hand
Adjust or retrain a model	"Add a minimum-age eligibility rule to my conversion outcome and kick off a rebuild."	Agent updates the outcome and starts the rebuild	Reconfigure it manually in the UI
Deploy an audience to a destination	"Build an audience of my top 20% churn-risk customers and send it to our Meta connection."	Agent creates the target and ships it	Build the target, map the fields, deploy yourself
Set up a new client account	"Spin up a new account for Acme, import their customer CSV, and configure a churn model."	Agent joins the account and configures it end to end	Repeat the whole build by hand, per client
Know what an account was built to do	"What use cases is this account set up for, and what problem is each one solving?"	Agent reads the use-case notes and reasons from them	Piece it together from memory and tribal knowledge

Getting connected

However you'll use it, connecting takes two steps:

1. Add the server.

In Claude (Desktop, Code, or claude.ai), add a custom connector pointing at https://mcp.faraday.ai/mcp. Most other MCP-compatible clients, including Cursor, take the same URL in their settings.

2. Sign in and confirm.

A browser window opens to authorize the connection to your existing Faraday account. Once it's approved, ask the agent to run whoami to confirm it and see every tool available.

No opt-in, no separate setup, no new account — it's on every Faraday account today. Full details are in the MCP docs.

Time to get connected

Already on Faraday? Connect your agent at mcp.faraday.ai/mcp and take your automation to the next level.

And if you’re new to Faraday, welcome! You can get started today on our self service data platform, Magenta. If you’d rather talk to someone about getting your tools set up, click here to connect with a Context Consultant.

FAQ

What is Faraday's MCP server?

It's a hosted, authenticated Model Context Protocol server that connects an AI agent directly to your Faraday account. The agent can read your data, analyze your models, and configure your account — cohorts, outcomes, connections, targets — in natural language.

Which AI agents work with it?

Any MCP-compatible client. That includes Claude (Desktop, Code, and claude.ai) and Cursor, and it uses the same standard other agents like ChatGPT are adopting.

Do I need to set anything up first?

No. You need an existing Faraday account — the server connects an agent to an account you already have, it doesn't create one — but there's no setup beyond connecting and signing in.

Can one agent manage multiple Faraday accounts?

Yes. With access to a parent account and its subaccounts, the agent can list every account it can reach and switch between them without a new connection.

How does authentication work?

Through OAuth, in your browser. The agent never handles an API key directly; you authorize it the same way you'd approve any other connected app.