Bankless Onchain MCP Server

The Bankless Onchain MCP Server bridges the gap between AI assistants and real‑world blockchain data by exposing a rich set of on‑chain operations through the Model Context Protocol. Instead of hard‑coding API calls or parsing raw RPC responses, developers can ask an AI model to “read the state of a contract” or “fetch transaction history,” and the server translates those high‑level intents into precise blockchain queries. This streamlines workflows that involve smart contract analysis, audit automation, or portfolio monitoring.

At its core, the server offers three logical categories of operations: Contract, Event, and Transaction. The contract tools let an AI read arbitrary contract state, discover proxy implementations, and pull ABI or source code for verified contracts. The event tools provide filtered log retrieval and the ability to compute event topic hashes, enabling AI‑driven monitoring of on‑chain events. Finally, the transaction tools expose historical transaction data and detailed receipt information, allowing models to trace user activity or analyze gas usage patterns. Each tool is defined with clear input schemas, ensuring type safety and reducing runtime errors.

Developers benefit from the server’s structured data outputs. Instead of dealing with raw hex strings, the MCP returns typed values (e.g., addresses, uint256) that AI models can readily consume and reason about. This eliminates the need for additional parsing logic in the client, leading to cleaner codebases and faster iteration cycles. The server also supports multiple networks (Ethereum, Polygon, Base, etc.), making it a single source of truth for cross‑chain analysis.

Real‑world use cases include:

• Smart contract auditing – AI assistants can automatically read a contract’s storage layout, verify that proxy addresses match expected implementations, and flag discrepancies.
• Portfolio analytics – By fetching transaction history for a wallet, the model can generate spend reports or detect unusual activity.
• Event monitoring – Developers can set up alerts that trigger when specific events (e.g., token transfers, governance votes) occur, with the AI summarizing or acting on those events.
• Compliance checks – Regulatory teams can query on‑chain data through an AI interface to confirm that contracts adhere to required standards.

Integration with existing AI workflows is straightforward. An MCP client (such as Claude or any other model that supports the protocol) simply invokes a tool name and supplies the required parameters. The server handles authentication with the Bankless API, performs the underlying RPC calls, and returns a well‑structured JSON response. Because the MCP abstracts away low‑level details, teams can focus on higher‑level business logic while still leveraging the full power of blockchain data.