Introducing Beckn-MCP Integration: Open Commerce for AI Agents

[RakeshVT]

Pre-submission Checklist

• This post follows the MCP community guidelines

What would you like to share?

Hello MCP community! I wanted to introduce a new open-source project that bridges MCP with Beckn Protocol - an open interoperability specification for decentralized commerce networks.

What is Beckn Protocol?

[Beckn Protocol](https://beckn.org/) is an open protocol that enables location-aware, dynamic discovery of services across different domains. It's domain-agnostic, decentralized, and used across mobility, retail, food delivery, healthcare, and more. Think of it as a "HTTP for commerce" - a standard way for any buyer app to discover and transact with any seller without proprietary integrations.

The Beckn-MCP Bridge

We've built a reference implementation that allows any MCP-compatible AI assistant to interact with Beckn Protocol networks. Here's what this enables:

• AI assistants can discover and purchase real-world services through natural language
• Users can complete entire transactions without leaving their AI conversation
• Services from any Beckn-compatible provider become accessible to AI assistants
• The ecosystem remains open, preventing new AI gatekeepers or walled gardens

How It Works

The integration uses three key technologies:

1. MCP - For AI assistant communication
2. Goose (Block's orchestration framework) - For workflow management
3. Beckn Protocol - For service discovery and transactions

When a user asks something like "Book me a cab to the airport," the flow works like this:

1. AI assistant sends the request via MCP to our server
2. An intent mapper identifies the domain (mobility) and extracts parameters
3. Goose orchestrates the workflow (search → select → confirm)
4. A Beckn client discovers available services from mobility providers
5. Results flow back to the AI for presentation to the user

Project Status

We've built a functional reference implementation including:

• MCP server with proper request/response handling
• Intent mapping for mobility, retail, and food domains
• Integration with Block's Goose framework
• Beckn client with domain-specific request handling
• Documentation and examples

Interesting note: I put this entire implementation together in under an hour using Claude and MCP, despite not being a developer myself. So while the code should be functional, I haven't thoroughly reviewed every aspect yet - community eyes and improvements are very welcome!

Seeking Collaboration

We'd love to collaborate with the MCP community on:

1. Improving intent mapping - Particularly with more sophisticated NLU
2. Extending domain support - Beyond our initial implementations
3. Testing - With different AI assistants and use cases
4. Best practices - For context preservation and multi-step transactions

If you're interested in bringing real-world transaction capabilities to MCP-compatible AI assistants, please check out the repo or reach out to me directly!

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I believe this integration represents an exciting opportunity to bring open commerce to AI agents, ensuring the AI ecosystem remains open, decentralized, and interoperable. Looking forward to your thoughts!

Relevant Links

https://github.com/mindincorg/MCP-Beckn

[tamal-thetaonelab]

How can we have an interface for ONDC (Open Network for Digital Commerce) which is based on Beckn itself.

[ankitShogun]

Hey @RakeshVT,
I'm Ankit from the FIDE (Beckn) tech team. We came across your initiative and found it really interesting — it's quite aligned with something we're currently working on. We'd love to connect and explore potential synergies. Could you please share your email and contact number for a quick sync-up?

[starkk242]

Hello @RakeshVT ,

This seems like a really interesting approach to taking ecommerce to the next level. I'm quite intrigued by the idea and would love to explore how it all works under the hood.

However, I noticed that the repository appears to be private. Would it be possible for you to provide access so I can take a closer look at the implementation?

Thanks for sharing this innovative concept!

[anish-9999]

Hey, very interesting idea! I myself am tinkering with it. However, when I tried to access the repo and it gave an error. Can you share it again?