An MCP server that gives orchestrator agents fine-grained control over interactive Claude Code sessions running inside tmux, enabling mid-session steering, interruption, and token-efficient result extraction.
A fault-tolerant, stealth-enabled Model Context Protocol (MCP) server for web searching and content fetching. Built for AI Agents (Cursor, Claude Code, OpenCode), it uses a stealth browser engine to fetch pages, dynamically handles SPAs/React, and converts bloat into token-optimized Markdown.
MCP server that gives AI coding agents (Claude Code, Cursor, Cline, etc.) access to multiple AI models through Antigravity CLI and OpenAI Codex CLI, enabling mid-conversation model consultation and code review.
Seven remote MCP servers exposing 51 published Apify scrapers as agent tools: company diligence, social listening, recruiting, real estate, lead generation, e-commerce and academic research. Billed per result, and a call that returns nothing is never charged.
This server lets MCP clients delegate coding tasks to a locally installed Qwen Code CLI, enabling second opinions, parallel work, and clean separation from the main context.
Git-native Markdown content management CLI with a built-in MCP server. Lets AI clients (Claude, Cursor) search, read, write, and govern story/paper/note repositories, auto-generate READMEs, validate configs, and export EPUB or SFT training data.
Post-hoc session handoff and project memory for Claude Code: MCP tools list_sessions and handoff turn any session — even crashed or usage-limit-hit ones — into a clean handoff.md another model can continue from, with secrets redacted from every output. Zero dependencies, fully local; also distills a project's entire session history into one cited memory brief.
Enables searching and retrieving Claude Code conversation history via hybrid semantic and keyword search, allowing the agent to access its own past interactions.
Relays messages between two Claude Code sessions running on separate machines, using the Channels API to push an incoming message directly into the other session so one session can ask another about its own checkout. Each message carries an id and is tracked as sent, delivered-to-process, or answered, with a per-message reply budget.
Enables AI-assisted code review with bias mitigation strategies through cross-model evaluation and bias-aware prompting. Detects AI-generated code from commit authors and provides structured reviews with security, performance, and maintainability analysis.