"sage active" matching MCP servers:

Shared, code-grounded memory for developers and their coding agents. Capture a learning once and the whole team plus every agent recalls it; memory is grounded in your code and stored as git-tracked JSON reviewed in PRs, with citations validated on write and stale memory withheld from recall. Works with any MCP client.

Enables LLM applications to interact with Google's Jules AI coding assistant to manage repositories, coding sessions, and pull requests. It allows users to programmatically create tasks, approve plans, and communicate with the assistant during active coding sessions.

Exposes the active file and selection(s) from VS Code to MCP clients, enabling CLI coding agents to access current editor context.

A state persistence layer that enables seamless handoffs between different AI coding assistants by maintaining a shared context bus. It provides tools for tracking summaries, next steps, and active files to ensure continuity across development sessions.

Dynamic context and active engineering workflow for AI coding assistants, enabling project-aware rules, code review, TDD, and delivery orchestration.

Enables AI assistants to write, validate, debug, and execute AutoHotkey v2 scripts on Windows, with tools for syntax checking, script execution, active window inspection, library search, and live debugging via DBGp protocol.

Provides structured visual analysis tools for coding agents, enabling UI analysis, screenshot comparison, OCR, and crop-based refinement through VLM.

Enables safe coordination of Herdr coding agents with lease-scoped reviewer and writer lifecycles, semantic waits for agent states, and read-only topology inspection, while prohibiting arbitrary shell execution and unscoped lifecycle operations.

Delegates large file reads to a cheap worker model so coding agents avoid consuming context, with optional hook enforcement and per-call token savings.