Tokencompress A sub-2ms Go CLI and MCP sidecar that prunes AI agent tool context

Description and use

**Project Summary:**

Tokencompress is an open-source, sub-2ms Go CLI and MCP sidecar that efficiently prunes AI agent tool context. This project enables seamless connections between AI assistants and real tools and data through a standard protocol, facilitating standardized integrations. By leveraging Tokencompress, developers can easily connect AI agents to tools and ship Model Context Protocol servers.

**Value Proposition:**

Tokencompress offers significant value in connecting AI assistants to real tools and data through a standard protocol. This enables developers to streamline integrations and create more efficient workflows. The project's sub-2ms performance and Go CLI make it an attractive solution for developers looking to standardize their integrations.

**Adoption Path:**

To adopt Tokencompress, developers should first review the project's documentation, issues, and release cadence to ensure it aligns with their needs. Due to sparse integration signals, manual inspection is necessary before adoption. Developers should also verify the project's license, maintenance, and quality signals before using it in production. Once these checks are complete, developers can start integrating Tokencompress into their workflows.

**Production Readiness:**

Tokencompress is considered Medium production-ready, making it suitable for prototypes or internal workflows. However, developers should perform dependency and maintenance checks before using it in production.

Before adopting

Review the current source documentation, licensing terms, dependencies, and maintenance status against your intended use. [View the source](https://github.com/dburnett11155-rgb/Tokencompress).

Use in a workflow

Tokencompress A sub-2ms Go CLI and MCP sidecar that prunes AI agent tool context helps connect AI assistants to real tools and data through a standard protocol.

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.