The Model Context Protocol (MCP) & Its Significance for Blockchain Apps

AI applications today are capable of generating text, answering questions, and reasoning through complex prompts. But when it comes to acting autonomously and accessing live data, they’re still limited. AI agents often operate in silos, cut off from the real-time systems and data that power modern applications.

For developers, integrating AI assistants with external systems—like content repositories, customer databases, or internal applications—usually means building custom, one-off solutions. Each new integration adds more complexity, slows development, and increases maintenance overhead. The result? AI apps become siloed, outdated, and less useful over time.

Custom integrations are inefficient, expensive, and hard to scale. To solve this, developers need a standardized, secure protocol to connect AI assistants with external data and services.

That’s exactly what Anthropic’s Model Context Protocol (MCP) delivers. It provides a universal standard for AI assistants to securely access external data sources and tools—without needing bespoke integrations. And for Web3 developers, MCP has the potential to revolutionize blockchain-based AI applications.

Let’s dive in.

What Is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open and universal standard that allows AI applications to request and retrieve data from external sources in a secure, consistent way. Think of it like a universal adapter for AI apps: it removes the need to build custom integrations (or adapters) for every tool. In essence, MCP provides a common language that AI assistants and external data sources use to communicate.

Who created the model context protocol?

MCP was introduced in 2025 by Anthropic, a leader in AI safety and research. It has since gained traction among developers and organizations focused on AI integration. The protocol is open-source, with specifications and SDKs freely available for developers to adopt and contribute to. As such, MCP’s development is community-driven, and contributions are encouraged from enterprises, independent developers, and researchers.

MCP’s Key Features

The Model Context Protocol (MCP) is designed to address the challenges of AI integration with a set of core features that prioritize flexibility, security, and scalability. These features make MCP a robust solution for developers building AI applications that interact with external systems.

Open Standard

MCP is an open-source protocol, meaning its specifications and SDKs are freely available for developers to use, modify, and contribute to. Community members can propose improvements to the protocol, share experiences and use cases and create and publish new MCP servers for other systems. This openness fosters collaboration and innovation, ensuring the protocol evolves to meet diverse integration needs.

Secure and Controlled Access

Security is a cornerstone of MCP. The protocol includes built-in permission management and encryption, ensuring that data exchanges are secure and access is tightly controlled. This is critical for applications handling sensitive information, such as customer data or proprietary business insights.

Language and Platform Agnostic

MCP is designed to work seamlessly across programming languages and platforms. Whether you’re developing in Python, Java, or another language, or deploying on cloud, on-premise, or hybrid infrastructures, MCP provides a consistent interface for integration.

Supports Pre-Built and Custom Servers

MCP offers flexibility in how developers connect to external systems. Pre-built servers for popular tools like Slack, GitHub, and Postgres simplify integration, while custom servers allow developers to tailor MCP to proprietary or niche systems. This adaptability ensures MCP can meet the needs of any project.

How Does MCP Work?

The Model Context Protocol (MCP) uses a client-server architecture to standardize interactions between AI applications and external data sources. This design ensures secure, efficient, and scalable communication. Its architecture consists of three core components:

MCP Hosts: AI applications (e.g., assistants or agents) that initiate data requests. For example, a chatbot might request customer data from a CRM.

MCP Clients: Intermediaries that route requests from hosts to servers, handling authentication, encryption, and protocol compliance.

MCP Servers: Gateways to external data sources (e.g., databases, APIs). They process requests, retrieve data, and return it in a standardized format.

Let’s see how these 3 work in practice:

How MCP Hosts, Clients & Servers Communicate

1. Request Initiation: An AI host identifies a need for external data and sends a request to the MCP client.

2. Request Routing: The client determines the appropriate MCP server based on the request type and routes it securely.

3. Data Retrieval: The server processes the query, retrieves the data from the external source, and formats it into a standardized structure.

4. Data Is Available For Use: The server sends the structured data back to the client, which forwards it to the host. The host uses the data to perform its task, such as generating a response or executing an action.

MCP in the Blockchain AI App Ecosystem: Why It Matters

Since MCP lets AI assistants interact with internal databases and CRMs, developing software with AI and managing content, it’s a massive step forward for AI powered blockchain apps. Let’s dive into how it helps blockchain apps specifically:

Increased Interoperability

Integrating MCP within the blockchain ecosystem simplifies the development process by providing standardized protocols for AI and blockchain interactions. This standardization reduces the complexity traditionally associated with integrating AI functionalities into blockchain apps. This can accelerate development timelines while fostering innovation—with MCP developers can focus on creating unique features rather than building foundational integration components.

Enhanced Automation

By allowing AI agents to autonomously execute transactions and interact with smart contracts, MCP reduces the need for manual oversight and enables more responsive applications.

Improved Data Accessibility

Real-time data querying capabilities ensure that your application can access the most current blockchain information, leading to more accurate analytics and decision-making processes.

Model Context Protocol(MCP) in the Blockchain Ecosystem: Example Use Cases

Of course, the possibilities are endless when it comes to building autonomous AI agents when you have so much easy access to blockchain data and power to wield. However, here are a few examples of capabilities you can enable for AI powered blockchain apps with an MCP server.

Real-Time On-Chain Data Querying

AI agents powered by MCP can access live blockchain data, giving them the power to make informed decisions based on up-to-date information. For example, an AI agent could monitor transaction volumes, track wallet activity, or analyze token movements to provide insights or trigger automated actions. This capability is particularly valuable for DeFi applications, where real-time data is critical for optimizing trading strategies or managing liquidity pools.

Smart Contract Analysis

MCP allows AI agents to retrieve and analyze smart contract source code and Application Binary Interfaces (ABIs). This functionality enhances transparency and security by enabling developers to audit contracts programmatically, identify vulnerabilities, or verify compliance with predefined standards. For users, this means greater confidence in the dApps they interact with, as AI-driven audits can provide continuous oversight.

Autonomous Transaction Execution

One of the most transformative use cases for MCP in blockchain is enabling AI agents to execute transactions autonomously. By integrating MCP with blockchain networks, developers can create AI-driven workflows that operate without human intervention. For instance, an AI agent could automatically rebalance a portfolio, execute trades based on market conditions, or manage supply chain logistics through smart contracts. This level of automation aligns with the decentralized ethos of blockchain, reducing reliance on intermediaries and increasing efficiency.

Bridging AI and Blockchain with thirdweb's MCP Server

thirdweb's MCP Server v0.1 Beta represents a significant advancement in integrating AI agents with blockchain technology. This server enables any Model Context Protocol (MCP) compatible client to interact seamlessly with any Ethereum Virtual Machine (EVM)-compatible blockchain, thereby enhancing the interoperability and functionality of AI powered blockchain apps.

Our MCP Server’s compatibility with any EVM-compatible blockchain ensures that developers can build versatile applications capable of operating across multiple networks Ethereum, Polygon, Binance Smart Chain, and other EVM-based chains. This flexibility is crucial in a fragmented blockchain landscape, where interoperability is often a barrier to adoption.

With the MCP client and server configured, your decentralized application can utilize the following thirdweb services:

• Insight: Access real-time on-chain data, analyze transactions, and retrieve smart contract details to enhance the analytical capabilities of your dApp.

• Engine: Enable autonomous interaction with deployed contracts, manage transactions, and perform on-chain operations without manual intervention, thereby increasing the efficiency of your application.

• Nebula: Implement complex on-chain tasks, generate smart contract code, and conduct in-depth contract analysis to expand the functionality and robustness of your blockchain app.

Learn more about thirdweb's MCP server in the changelog ->

How to Use MCP in Your Blockchain Application

Want to start using the thirdweb MCP server in your blockchain app? Here’s how to get started:

1. Sign in to (or create) your thirdweb account

2. Obtain a thirdweb secret key via the dashboard.

3. Configure your MCP client

   1. Connect your MCP client to the thirdweb MCP Server

   2. Modify your client's configuration file to include the server details. For instance, if using Claude Desktop, update the claude_desktop_config.json file with the server's command and environment variables.

   3. Ensure that the necessary environment variables, such as THIRDWEB_SECRET_KEY, are correctly set to establish a secure connection between your client and the server.

4. Get building!

The Future of the MCP

The Model Context Protocol (MCP) solves a key challenge in AI development: how to connect AI assistants to external tools and data sources easily, securely, and at scale. By providing a standard interface, MCP helps developers reduce complexity and focus on building AI applications that deliver real value. Whether you’re integrating with internal systems or building AI-powered tools for customers, MCP offers a future-proof and community-supported approach.

To support innovation in the space, thirdweb is proud to integrate Anthropic’s standard, along with other early adopters—especially to represent the web3 developer ecosystem.

Want to learn more about how thirdweb is getting involved with MCP? Check out the details of our MCP server in the changelog here. And if you want to know more about MCP itself, check out the official GitHub page here.