"Files formatted in JSON" matching MCP servers:

Enables running one Codex CLI task at a time in an isolated temporary folder, returning Codex's answer and a manifest of produced files for manual review and selective copying.

Simple sequential thinking MCP in python

* taniwhaai/arai 🦀 🏠 🍎 🪟 🐧 - Policy enforcement for AI coding agents derived from existing instruction files (CLAUDE.md, .cursorrules, .windsurfrules, .github/copilot-instructions.md) — no separate YAML to maintain. Rules with prohibitive predicates (never, forbids, must_not) emit permissionDecision: deny to block tool calls in Claude Code; advisor

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.

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.

Allows two AI coding agents on different machines to securely pair and share files, context, and conventions through an end-to-end encrypted peer-to-peer channel with human-in-the-loop consent.

Enables parallel execution of shell commands and AI coding agents (Claude, Gemini, Codex) across lists of items like files or URLs, with batched processing and real-time streaming output for batch operations.

Provides coding agents with repository context through tools that generate AGENTS.md files or structured JSON summaries, quoting build/test commands from the repo's manifests for a small fee without an API key.

Generates AnyLogic simulation models from natural-language prompts in Claude Code, allowing users to describe queueing systems, factories, or ERs and obtain runnable .alp files.

A comprehensive MCP server providing tools for AI agents to interact with code, including reading symbols, importing modules, replacing text, and sending OS notifications.

Enables agents to quickly find and edit code in a codebase with surgical precision. Find symbols, edit them everywhere with tools for reading code blocks, searching/replacing text, and making precise line-based modifications.

A task-level STDIO MCP server that lets Codex or any other MCP client hand off scoped coding jobs to an asynchronous worker agent which reads the code, edits files, and runs tests, while the client keeps ownership of planning and acceptance. Exposes submit, wait, query, follow-up, and cancel tools so multiple clients can queue and monitor tasks against a chosen project root.

A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.

A Model Context Protocol server that connects to a WISP game panel, letting AI clients inspect and manage game servers (state, files, console, backups, databases) with read-only defaults and opt-in capabilities.

MCP server for the AIMFP paradigm, enabling AI assistants to manage functional programming projects with persistent database state, directive compliance, and task tracking via JSON-RPC tools.

A domain-agnostic MCP server for autonomous experimentation, generalizing Karpathy's autoresearch pattern into a reusable server that any AI agent can drive, pointed at any domain defined by a JSON configuration.

Enables Claude.ai to interact with the Cursor editor to read files, write code, get selections, and more.

Lets coding agents create and manage threads in a running T3 Code server over MCP.

Generates compiled Flutter apps from JSON Product Requirements Documents. Exposes endpoints for PRD validation, schema, payment terms, and build status.

Enables coding agents to open files and git diffs directly in VS Code, allowing users to review code changes without leaving their conversation.