MCP middleware that stops your agent from drowning in tool schemas

What it does

Klavis is an MCP integration platform that connects AI agents to external services. It offers three products: Strata, which presents multiple MCP servers as a single connector to reduce context window usage; over 100 prebuilt integrations with OAuth support; and an MCP Sandbox for LLM training and reinforcement learning at scale. You can consume it as a cloud-hosted service, self-hosted Docker containers, or through Python and TypeScript SDKs.

The interesting bit

Strata acts like a multiplexer for tool schemas. Instead of an agent loading definitions for dozens of separate services and burning through its context window, it talks to one consolidated endpoint. The README is vague on exactly how the optimization works—whether it filters, compresses, or simply routes—but the premise is sound: more tools usually means more tokens.

Key highlights

• 100+ prebuilt integrations (GitHub, Gmail, Slack, etc.) with OAuth handling
• Strata consolidates multiple MCP servers into a single interface
• Four access modes: cloud-hosted, self-hosted Docker containers, SDK, or REST API
• MCP Sandbox for scalable LLM training and reinforcement learning environments
• Both Python and TypeScript SDKs available

Caveats

• The README reads like a product landing page; it offers little detail on Strata’s consolidation mechanics or the sandbox’s architecture.
• It is unclear which components are fully open source versus cloud-only services.

Verdict

Worth a look if you’re building agents that need to interact with many SaaS tools and you’re hitting context limits. Skip it if you were hoping for a deep-dive open-source framework to study—the README is a landing page that points to hosted services and installable binaries rather than explaining internals.