As Web3 and AI continue to converge, one of the biggest challenges for developers is giving intelligent systems more than just raw blockchain data. Today’s APIs can surface transaction hashes, but they don’t provide the kind of context an AI needs to reason, act, and deliver real value.
Tairon is aiming to change that. Its newly launched MCP Supergraph extends the oracle model from price feeds to intelligence, connecting on-chain data to AI systems so they can query, verify, and act in real-time. Developers can tap into wallets, protocols, and computation through one oracle layer, while providers keep ownership and earn from every request. Unlike centralized platforms, Tairon delivers transparent, verifiable signals with provenance, setting the new standard for oracles in the AI era.
To unpack what this means for builders and the broader Web3 × AI ecosystem, Blocktelegraph spoke with Danyl Denk, Full Stack Engineer at Tairon. Danyl works on Tairon’s developer-facing surfaces, building the tools that make publishing MCP servers onchain seamless and accessible to engineers. He is responsible for updating the Tairon dashboard, leveraging back-end tools for fast responses and working directly with developers to ensure a seamless bridge between protocol logic and practical workflows.
What problem does the MCP Supergraph solve that existing data access solutions don’t, and why is this a breakthrough for Web3 × AI?
Existing solutions give AI agents raw blockchain data. We give them context and intelligence. Current APIs return transaction hashes – we return “Your Uniswap position is underwater by 12%.” It’s the difference between giving AI a phone book vs. giving it a personal assistant who knows everyone in town.
You describe MCP Supergraph as “AI-native by design.” What does that mean in practice for LLMs and agents working with decentralized data?
Natural language queries, contextual responses, and reasoning-optimized formats. Instead of “call 15 APIs to check DeFi positions,” AI agents ask “What’s my risk exposure?” and get structured intelligence they can immediately reason about. Built for LLM consumption, not human developers.
How does the on-chain registry and open server directory standardize publishing and discovery of MCP servers across multiple chains?
Universal MCP standard + automated validation + reputation scoring. Developers publish once, works everywhere. Our registry validates functionality, monitors uptime, and scores reliability. Think npm for blockchain data, but with quality guarantees.
Developers are often the bottleneck in adoption. What makes running and managing an MCP server simple enough for builders to pick up right away?
One-click deployment, auto-generated templates, and plug-and-play SDKs. We provide the boilerplate, you add protocol logic. Deploy to our network in <5 minutes. Full monitoring, scaling, and maintenance are handled automatically.
You’ve already integrated with nearly 100 protocols. What are some of the earliest use cases you’re seeing, and how will you scale to 300 by the end of the year?
Top use cases include AI portfolio managers, automated risk monitoring, cross-chain yield optimization. Scale strategy: Partner with protocols directly – we build their MCP, they co-market. Two new integrations/week, plus community contributions.
What role does the $TAIRO token play in coordinating infrastructure and governance while ensuring quality and reliability across the Supergraph?
Quality staking, governance, and network incentives. MCP operators stake $TAIRO for reliability bonds. Token holders vote on standards and upgrades. Revenue sharing rewards high-quality servers. Aligns entire network toward reliability and growth.
Closing Thoughts
With the launch of the MCP Supergraph, Tairon is introducing a new approach to connecting AI systems with blockchain data. By emphasizing standardized publishing, verifiable signals, and developer-friendly tooling, the project aims to move beyond raw data delivery toward actionable intelligence. As Tairon scales from nearly 100 protocols today toward a target of 300 by year’s end, its progress will be a key indicator of how effectively Web3 and AI can converge.
