Lessons from Scaling GitHub's Remote MCP Server#
AI Engineer Europe 2026 digest Program Core pick 8–10 April 2026
Sam Morrow — GitHub
- Why: One of the most heavily used MCP servers in the ecosystem (4M+ downloads of the stdio server alone) — real production experience scaling AI-serving infrastructure, with classic cloud-ops problems: stateless horizontal scaling, distributed session storage, and observability under privacy constraints.
- What: The architectural decisions and lessons from initial implementation to horizontal scaling of a remote MCP server: managing tool overload, optimizing context usage, distributed session storage, observability without compromising user privacy, and concrete patterns and anti-patterns.
- How: Stateless, horizontally scalable server design; condensing an expansive platform (GitHub) into a coherent MCP interface; auth, security, and privacy treated as first-order concerns.
- Where applicable: Anyone building or operating remote MCP servers on production infrastructure — broadly applicable patterns, GitHub-specific in scale details.