Carbon Arc MCP Server

**Overview**

Carbon Arc is the infrastructure for the AI economy. We structure the 90% of private data that sits unanalyzed into composable, queryable, monetizable primitives — built for inference, agents, and decisions. Our patented stack converts heterogeneous raw data into pre-mapped, queryable intelligence delivered through a flexible, consumption-based model. The Carbon Arc MCP Server brings that catalog directly into Databricks, letting any user — business or technical — interact with Carbon Arc through natural language for conversational data search, multi-source synthesis, and reporting. Ask *"What was Walmart's card spend in Q1 2024?"* and the MCP resolves the entity, picks the right data assets, and returns a structured, model-ready answer. The platform carries hundreds of data assets and petabytes of data spanning tens of thousands of companies, hundreds of thousands of brands, and trillions of transactions — with new assets added every month.

What ties it all together is the **Carbon Arc unified ontology**. We structure data against four core entities — **Companies** (e.g., Alphabet), **Brands** (e.g., Google), **People** (e.g., Taylor Swift), and **Locations** (e.g., New York) — and link each one to operational representations like retailers, apps, and websites. That cross-walk between datasets means every query lands on the right entity and surfaces a more complete picture of an organization with zero additional effort.

**To get an API Token Visit**
1. Sign up for Carbon Arc at - https://app.carbonarc.co/pricing
2. Log into your account
3. Get your API Token from your user profile

**Use cases**

- **Investment research and earnings prep** — pivot seamlessly between ticker, company, and brand views to forecast performance, benchmark competitors, and surface YoY trends across consumer spend, hiring, and web traffic.
- **Brand, marketing, and consumer intelligence** — connect a brand to its app, website, and physical locations in a single query to measure campaign ROI, audience reach, and customer behavior.
- **Sports, media, and entertainment** — optimize ticket pricing, identify fan and audience preferences, and turn local market signals into new revenue opportunities.
- **Strategy consulting and due diligence** — size markets, profile competitive landscapes, and pressure-test investment theses with entity-resolved, model-ready evidence.
- **Sector and macro analysis** — roll signals up from location to brand to company to sector across retail, QSR, healthcare, autos, media, sports, and more.
- **AI and enterprise applications** — feed structured, ontology-resolved insights into LLMs, agents, and downstream models without SQL or ETL drag.

**Product details**

- **Entity types** covered include Companies, Brands, People, and Locations — each linked through the unified ontology to representations like retailers, apps, websites, and categories so disparate datasets cross-walk into a single, coherent view.
- **Example datasets** include US & Canada Card Spend, EU Card Spend, US Detailed Card, Point-of-Sale (Instore + Online), Medical + RX Claims, Advertising, Web Traffic, Job Listings, App Activity, and Ticket Sales — with new assets like EMEA invoices, intraday credit card, closed medical claims, OTR trucking volume, drone footage, and supply chain transcripts on the near-term pipeline.
- **Sample fields** include Entity (company, brand, location, person, retailer, app, or website), Insight (e.g., Card Spend, Revenue, Ad Spend, Web Traffic, Hiring), Time Period, Geography, Demographic Cohort, Payment Type, Confidence Score, and Growth Metric.