Gopher & Gemini MCP Server

The Gopher & Gemini MCP Server is a purpose‑built bridge that lets modern large language models (LLMs) such as Claude explore the niche but richly structured worlds of Gopher and Gemini. By exposing these vintage protocols through the Model Context Protocol, developers can give their assistants a window into “alternative internet” communities—everything from early‑internet news feeds to niche discussion boards and static content hosted on Gopher servers, as well as the newer Gemini space that offers secure, lightweight web‑like experiences. This solves a key problem: traditional web crawlers and LLM APIs are tuned for HTTP/HTTPS, leaving the vast repositories of Gopher/Gemini data inaccessible to AI assistants without custom tooling.

At its core, the server implements two main tools— and . Each tool understands the full spectrum of protocol‑specific content types, from simple text files and binary blobs on Gopher to gemtext documents that include inline links and formatting on Gemini. The responses are returned as structured JSON, making them immediately consumable by an LLM without additional parsing. Because the server is built on FastMCP, it leverages asynchronous I/O and caching to keep latency low even when traversing deep Gopher menus or handling large Gemini documents.

Security is a cornerstone of the design. The server enforces TLS for all Gemini connections, supports TOFU (Trust On First Use) certificate validation, and allows optional client certificates for authenticated Gemini sites. It also applies host allowlists, request timeouts, and size limits to guard against accidental over‑fetching or malicious content. These safeguards mean developers can safely integrate the MCP into production workflows without exposing their systems to untrusted data streams.

Typical use cases include building knowledge‑base assistants that pull from archival Gopher sites, creating educational tools that surface gemtext tutorials, or enabling chatbots to answer niche questions sourced from specialized Gemini forums. Because the MCP exposes both protocols as first‑class tools, developers can compose multi‑step reasoning: a model might first list relevant Gopher menus, then fetch the chosen entry, and finally retrieve supplementary Gemini resources—all within a single conversation. This tight integration streamlines AI workflows and opens up new avenues for content discovery that were previously siloed behind legacy protocols.