Introducing Bud’s MCP server: Build AI agents that actually understand money

AI in finance is moving fast, but most teams hit the same wall: Getting high-quality context into agents safely and quickly. That’s why we’re introducing the Bud MCP server, our implementation of the Model Context Protocol (MCP) that gives developers and financial institutions a standardized way to plug their core customer data into AI experiences.

If you’re experimenting with agents, assistants or autonomous workflows, you’ve likely discovered that models without financial context produce vague, brittle answers. The difference between a delightful experience and a dead-end chat exchange comes down to whether the agent can see and reason about the right data, at the right time, with the right controls. That’s exactly what Bud’s MCP server delivers.

What is MCP and why should banks care?

Model Context Protocol is an open standard that lets AI applications connect to external systems, tools and services in a consistent and secure way. Instead of bespoke connectors per project, MCP offers a uniform way to expose trusted capabilities to an AI runtime. 

For financial services, that standardization matters. It means:

• Faster time to value: Stand up proof-of-concepts in days, not months.
• Safer access: Scoped, consent-aware sessions make it easier to respect policies and permissions.
• Repeatable patterns: The same approach works for customer service assistants, product research tools and internal decisioning assistants.
  ‍

Bud’s layer: Financial intelligence, ready for agents

Bud’s mission is to simplify financial decisions by turning raw banking data into rich customer insights. Our AI platform interprets transactions in real time to build a clear, explainable picture of a person’s financial life – income, merchants, categories, commitments, affordability markers, life events, trends and more.

With the Bud MCP server, that intelligence is available to agents through a standardized set of tools and resources. That means agents can now:

• Answer questions about spending patterns, e.g. “Why were my card expenses higher in August?”
• Support product and suitability journeys
• Locate and explain individual transactions (merchant, category, location, metadata)
• Summarize affordability and cash-flow
• Trigger money-management actions, such as nudges, savings progress checks and budgeting insights 
  ‍

Context is the bottleneck, not models

So, why now? The shortfall in most AI roadmaps comes down to operationalizing quality data. Teams struggle to wrangle inconsistent feeds, clean transactions, and reconcile identifiers across systems. The results are familiar: Slow builds, manual exceptions and hallucinations when agents reason without ground truth.

Bud solves this at the source with a market-leading transaction enrichment engine and proprietary financial models. MCP then packages that capability into a developer-friendly, standardized framework so your teams can build once and reuse everywhere. 
‍

Real examples you can ship today

• Customer service agent assist: Empower agents to resolve disputes in real time. Surface the disputed transaction with merchant enrichment, category, location, and similar transactions, plus context that flags subscriptions, refunds, or known merchant descriptors.
• Personal finance copilot: Let an individual user connect a local AI app to plan a vacation that considers savings progress, upcoming bills, and available balances, with transparent reasoning and user consent.
• Rapid product experimentation: Spin up internal tools that analyze segment‑level behavior and test targeted propositions using trusted signals; then graduate the winners to production channels without rebuilding the data plumbing.
  ‍

The bottom line

MCP gives AI a common language for tools. Bud gives those tools financial intelligence that’s explainable, secure and production-ready. Put them together and you have the fastest path from AI idea to implementation in financial services. 

Ready to build? Get in touch to learn more about MCP and how we can support your AI transformation journey.