Transparent AI Service Invocation through MCP and Blockchain Integration

Abstract

This white paper presents a novel framework for integrating artificial intelligence (AI) assistants with external services using the Model Context Protocol (MCP), augmented by blockchain technology to ensure transparent and immutable logging of service invocations. The proposed system automatically invokes MCP services in response to user queries and records these interactions on a blockchain, providing a verifiable audit trail. By combining MCP’s standardized interface with blockchain’s decentralized ledger, the system enhances trust, accountability, and interoperability in AI-driven processes. This paper details the system’s architecture, technical implementation, benefits, potential use cases, and challenges, offering a robust solution for transparent AI-service interactions.

1. Introduction

The rapid advancement of AI has led to the widespread adoption of AI assistants capable of addressing diverse user queries. However, these systems are often constrained by static training data, lacking the ability to access real-time or context-specific information from external sources. The Model Context Protocol (MCP) provides a standardized solution, enabling AI models to connect seamlessly with various data sources and tools. As AI increasingly influences critical decision-making, transparency and auditability have become essential requirements.

This white paper proposes a system that integrates an MCP client with multiple MCP services, automatically invoking these services based on user queries, and transparently logging the process on a blockchain. This approach not only extends the capabilities of AI assistants but also ensures that every interaction is traceable and verifiable, fostering trust and reliability. The paper is organized as follows: Section 2 provides background on MCP and blockchain; Section 3 describes the proposed solution; Section 4 covers technical implementation; Section 5 discusses benefits and use cases; Section 6 addresses challenges and mitigations; and Section 7 concludes with future directions.

2. Background

2.1 Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard designed to facilitate the integration of AI models with external data sources and tools. Often likened to a "USB-C port for AI applications," MCP offers a uniform interface that simplifies connectivity across diverse systems. Its client-server architecture includes:

• MCP Hosts: AI applications requiring external capabilities.

• MCP Clients: Maintain connections to MCP servers.

• MCP Servers: Lightweight programs exposing specific data sources or tools. This structure enables AI assistants to dynamically access a wide range of services, such as databases, APIs, or specialized tools, enhancing their contextual responsiveness .

2.2 Blockchain Technology

Blockchain is a decentralized, distributed ledger technology that ensures data integrity and transparency through cryptographic hashing and consensus mechanisms. Its defining features—transparency, immutability, and decentralization—make it an ideal platform for recording AI-service interactions. Once data is recorded in a block, it cannot be altered without consensus, providing a tamper-proof log that supports auditing and verification.

3. Proposed Solution

The proposed system integrates an MCP client with various MCP services, enabling automatic service invocation in response to user queries, with all interactions transparently recorded on a blockchain.

3.1 System Architecture

The system consists of five key components:

1. AI Assistant: The user-facing interface, powered by a large language model.

2. MCP Client: Connects the AI assistant to MCP servers.

3. MCP Servers: Provide access to diverse services (e.g., weather APIs, databases).

4. Blockchain Network: A decentralized ledger for logging invocations.

5. Service Invocation Logger: Captures and records interaction details on the blockchain. When a user submits a query, the AI assistant processes it, the MCP client invokes the appropriate service, and the logger records the transaction on the blockchain.

3.2 Service Invocation Mechanism

The system employs natural language understanding (NLU) to analyze user queries and determine the required service. A service mapping database associates query intents with specific MCP servers. For instance, a query like "What’s the weather in New York?" triggers an MCP server providing weather data. The MCP client then executes the call and retrieves the response.3.3 Blockchain IntegrationEach service invocation is logged as a blockchain transaction, including:

• Timestamp

• Hashed user query (for privacy)

• Invoked service

• Parameters

• Response

• AI’s final answer This ensures a comprehensive, immutable record of every interaction, accessible for verification by authorized parties.

4. Technical Implementation

4.1 MCP Client Configuration

The MCP client is configured to connect to multiple MCP servers via a JSON configuration file. An example is shown below:json

{  
  "mcpServers": {  
    "weather": {  
      "command": "weather-mcp-server",  
      "args": ["--api-key", "YOUR_API_KEY"]  
    },  
    "database": {  
      "command": "database-mcp-server",  
      "args": ["--connection-string", "YOUR_DB_CONNECTION"]  
    }  
  }  
}  

This configuration enables the AI assistant to access a variety of services seamlessly.

4.2 Service Discovery and Invocation

Upon initialization, the MCP client queries connected servers to discover available tools and their specifications. When a user query is received, the AI assistant’s NLU module identifies the intent and matches it to a service. The MCP client then constructs the necessary parameters and invokes the service, returning the result to the AI assistant.

4.3 Blockchain Transaction Structure

A permissioned blockchain is used to balance transparency and performance. Each transaction follows this structure:json

{  
  "timestamp": "2025-04-23T12:08:00Z",  
  "query_hash": "sha256(user_query)",  
  "service": "weather",  
  "parameters": {"location": "New York"},  
  "response": "Sunny, 25°C",  
  "ai_answer": "The weather in New York is sunny with a temperature of 25°C."  
}  

Hashing the query protects user privacy, while the permissioned network ensures efficient transaction processing.

5. Benefits and Use Cases

The system provides several advantages:

1. Transparency: Interactions are publicly verifiable on the blockchain.

2. Auditability: Immutable logs enable detailed auditing.

3. Accountability: Errors or disputes can be traced and resolved.

4. Interoperability: MCP supports integration with diverse services. Potential applications include:

• Customer Support: Transparent responses to inquiries.

• Financial Services: Auditable AI-driven financial advice.

• Healthcare: Verifiable AI-assisted diagnoses.

6. Challenges and Mitigations

6.1 Scalability

High transaction volumes could overwhelm the blockchain. Mitigations include:

• Adopting layer 2 scaling solutions.

• Batching multiple invocations into single transactions.

6.2 Privacy

Recording queries, even hashed, raises privacy concerns. Solutions include:

• Zero-knowledge proofs to verify interactions without revealing details.

• User opt-out options for sensitive queries.

6.3 Performance

Blockchain logging may introduce latency. Optimizations include:

• Asynchronous logging to avoid delays.

• Caching frequent responses to reduce invocations.

7. Conclusion

This white paper introduces a groundbreaking system that integrates MCP with blockchain technology to enable transparent AI service invocation. By automating MCP service calls and logging them immutably, the system addresses the critical need for trust and accountability in AI applications. Future research will focus on enhancing privacy, optimizing blockchain scalability, and expanding MCP service coverage. This approach sets a foundation for reliable, auditable AI systems, paving the way for broader adoption across industries.