Overview of the MCP Snapshot Server

The MCP Snapshot Server bridges the gap between AI assistants and the Snapshot.org governance platform. By exposing a set of MCP‑compliant tools, it allows assistants such as Claude to query Snapshot spaces, proposals, and user data in a structured way. This eliminates the need for developers to write custom API wrappers or manage authentication tokens, giving them a ready‑made interface that adheres to the Model Context Protocol’s expectations for resources, tools, and prompts.

At its core, the server offers a handful of high‑level actions: retrieving lists of Snapshot spaces (, ), fetching proposals for a particular space (, ), and obtaining user information by Ethereum address (). Each tool accepts simple, optional parameters (e.g., pagination limits, state filters, search terms) that map directly to Snapshot’s own query capabilities. The result is a clean, declarative API where the assistant can ask for “the top 10 active proposals in the DAO space ” and receive a JSON payload without any manual plumbing.

For developers building AI‑powered governance assistants, this server is invaluable. It removes the friction of handling Snapshot’s GraphQL or REST endpoints, authentication flows, and data normalization. Instead, the MCP server presents a uniform tool set that can be invoked through standard MCP calls, making it trivial to embed governance queries into conversational flows or automated workflows. The server’s design also supports pagination and filtering out of the box, enabling assistants to handle large data sets gracefully.

Key capabilities include:

• Space discovery: List all Snapshot spaces or retrieve a ranked, filtered subset.
• Proposal management: Pull active, closed, or pending proposals for a given space, with optional limits.
• Proposal details: Fetch comprehensive information on a single proposal by ID.
• User insights: Retrieve Snapshot user profiles using their Ethereum address.

Typical use cases span from a DAO’s internal dashboard assistant that surfaces upcoming votes, to an analytics bot that monitors proposal performance across multiple spaces. In a workflow, a developer can configure the MCP server in Claude Desktop’s configuration file, then invoke these tools via natural language prompts. The assistant returns structured data that can be displayed in UI components, fed into dashboards, or used to trigger downstream actions such as sending alerts.

What sets this server apart is its tight integration with the MCP ecosystem. By conforming to MCP standards, it ensures compatibility across any AI client that supports the protocol, not just Claude. Moreover, its lightweight implementation in Node.js means developers can host it locally or deploy it to a cloud function with minimal overhead, keeping latency low for real‑time governance queries.