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Simple sequential thinking MCP in python

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

Receives UI feedback captured by the PickFix browser extension—element picks, inline text rewrites, page comments, and recorded bug steps—and hands it to a local coding agent to fix in the current repository, with screenshots and per-item results reported back to the extension. It also pairs the extension per machine, queues batches so only one session claims each, and keeps everything loopback-only and untrusted.

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

A project-local MCP bridge that allows Codex Desktop to plan tasks and OpenCode to execute them within the current project directory, with session reuse and native OpenCode background subagents.

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

Enables Codex to delegate code reviews, coding tasks, image/video generation, and background investigations to Grok via MCP tools.

Enforces a git-tracked, code-checked finite state machine that hands control between a planning director and a local Ollama implementor, exposing tools to check phase status, transition states, and run the implementor to execute plan step files one at a time. It enables a project to be planned, implemented step by step with per-step test gating, and validated before completion.

Graph-native bounded working memory for coding agents with typed memories, causal retrieval, current-vs-stale state queries, and compact small-model briefs.

Provides interactive visual plans for AI coding agents, allowing users to review, approve, and monitor execution in real-time.

Enables AI coding assistants to ask for human clarification and share thoughts in real-time, creating natural, engaging coding conversations.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

Lets users explicitly hand off coding tasks to a single DeepSeek sub-agent that writes directly into the current project without Git, while Codex handles task setup and reviews the resulting diff.

Persistent memory and pattern learning for AI coding assistants via MCP.

Enables a planning assistant to submit, inspect, and review project-scoped coding tasks while a local coding agent runs them in isolated workspaces through durable queues and read-only source access.

Enables Claude Desktop to spawn new Claude Code instances in iTerm2 windows for interactive coding sessions.

Enables AI agents to participate in CodeChef contests by fetching problems, generating and testing solutions in a secure sandbox, and submitting answers.

Enables launching Claude Code in a specified project directory by opening a new terminal session with optional initial prompt.

Connects Windows-based Codex to the Cursor Agent CLI in WSL, providing a run_cursor tool to execute tasks in specified working directories and resume sessions per directory.

Enables managing coding agent sessions in a shared workspace, supporting session registration, handoff notes, and export summaries via Streamable HTTP.