Etherscan MCP Server

The Etherscan MCP Server bridges the gap between large‑language models and real‑world blockchain data by exposing a rich set of Etherscan API endpoints through the Model Context Protocol. Instead of hard‑coding blockchain queries into an LLM’s prompt, developers can delegate data retrieval to this server and let the assistant focus on reasoning and synthesis. This is especially valuable in scenarios where up‑to‑date, chain‑agnostic information is required—such as portfolio analysis, audit support, or dynamic contract inspection.

At its core, the server implements a Go‑based Etherscan client that supports Etherscan API V2, giving it access to more than 50 supported chains. A single API key suffices for all these networks, simplifying credential management. The MCP interface translates natural‑language requests into structured calls to endpoints that fetch account balances, block details, contract ABIs and source code, gas prices, token metadata, transaction histories, and more. Because the server runs as an MCP service, it can be launched either via standard input/output (ideal for tightly coupled LLMs) or as a Server‑Sent Events endpoint, offering flexibility in how the assistant communicates.

Key capabilities include:

• Multi‑chain queries: seamlessly switch between Ethereum, Polygon, BSC, Arbitrum, and dozens of other chains without changing the assistant’s prompt.
• Rich data retrieval: balances, transaction lists, contract source and ABI, gas price oracles, token metadata, and block rewards.
• Customizable endpoints: the SSE mode exposes a single endpoint that can be integrated into existing web‑based LLM workflows or used with any client that supports Server‑Sent Events.
• Security and scalability: the server runs locally or in a container, keeping API keys out of user prompts while still delivering low‑latency responses.

Real‑world use cases abound. A financial analyst can ask the assistant, “Show me the token balance for USDT on BSC,” and receive an instant, verified answer without manual API calls. A smart‑contract developer might request the ABI of a deployed token to prototype interactions, while an auditor could track all transfers to a hot wallet across multiple chains. In educational settings, students can query historical block data or gas prices to understand network dynamics directly through the assistant.

Integrating the Etherscan MCP Server into an AI workflow is straightforward: configure the LLM’s MCP client to point to either the local binary (stdin/stdout) or the endpoint, and pass along the necessary API key via environment variables. From there, the assistant can issue natural‑language queries that are automatically routed to the server, which returns structured JSON responses. The LLM can then parse these results and weave them into coherent explanations, summaries, or code snippets—making the assistant a powerful partner for blockchain development and analysis.