DexScreener MCP Server: Real-Time DEX Pair Data for AI Applications

Overview: What is DexScreener MCP Server?

The DexScreener MCP Server is an implementation designed to provide real-time access to decentralized exchange (DEX) pair data, token information, and market statistics across multiple blockchains. This server sits as a bridge between AI applications such as Claude Desktop, Continue, and Cursor, leveraging the Model Context Protocol (MCP) for seamless integration with external APIs like DexScreener's API.

Core Features & MCP Capabilities

The DexScreener MCP Server offers several key capabilities that enhance the functionality of AI applications:

• Rate-Limited API Access: Ensures compliance with DexScreener's rate limits to maintain stable and efficient API usage.
• Comprehensive Error Handling: Provides standardized error codes and messages for robust handling of various scenarios, including rate limit exhaustion, invalid parameters, network errors, and API-specific issues.
• Type-Safe Interfaces: Implements strong typing in TypeScript to guarantee the reliability and predictability of method calls.
• Support for All DexScreener Endpoints: Offers a comprehensive suite of tools covering token profiles, boosted tokens, top boosts, pair information, order-related queries, and more.
• Integration Testing: Includes integration tests to ensure reliable performance and robustness.

⚙️ MCP Architecture & Protocol Implementation

The server architecture and protocol implementation are designed to seamlessly integrate with the Model Context Protocol (MCP). The core components include:

• API Service Layer: Contains TypeScript interfaces and service implementations for interacted endpoints.
• Tool Handlers: Define methods corresponding to specific endpoints, enabling precise API interaction through MCP calls.
• Error Handling Mechanisms: Standardized error handling ensures consistent communication and reliable operation.

Adding New Features

To add new features or tools:

1. Define types in src/types/
2. Implement service methods in src/services/
3. Add tool handlers in src/index.ts
4. Update documentation
5. Create tests to ensure robustness

The server is structured with the following folders:

• src/: Contains source code, including type definitions and service implementations.
• docs/: Holds documentation such as API references.
• scripts/: Contains setup and utility scripts for development.

💡 Key Use Cases in AI Workflows

Real-World Scenario 1: Token Price Monitoring

To monitor token prices across multiple DEXs, an AI application can utilize the get_token_orders method:

const result = await mcpClient.callTool('dexscreener', 'get_token_orders', {
  chainId: 'solana',
  tokenAddress: 'So1111111111111111111111111111111