"The role of planning in project development" matching MCP servers:

A stateful MCP server that remembers what your AI agent has already seen.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

A FastMCP server that exposes predefined artifacts as Resources and project lifecycle operations as Tools for creating, updating, deploying, debugging, testing, monitoring, and configuring projects with minimal token usage.

Enables rapid creation of new projects from predefined templates including React, Node.js, Django, Flask, and more. Provides comprehensive project scaffolding with file system operations, template management, and command execution capabilities.

Enables building, starting, stopping, restarting, inspecting, and monitoring registered Spring Boot + Maven projects on macOS, including status checks and log tailing through MCP tools.

Centralized MCP server that provides standardized development workflow commands to Cursor AI, auto-detecting project type (Laravel, Next.js, Flutter, Architecture) and offering relevant commands for implementation, testing, and commits.

Enables AI agents to autonomously navigate a codebase by listing directories, reading files, searching code, and running whitelisted commands.

Lets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.

A communication pipe and tool suite that enables AI models to interact with external systems through mathematical calculations, remote control, and data processing. It supports multiple transport protocols including stdio, SSE, and HTTP for flexible and extensible tool integration.

Exposes persistent, stateful remote Bash sessions to AI agents via SSH, enabling command execution with preserved working directory and environment.

Provides MCP access to plan and execute reproducible computational experiments with immutable execution plans, artifact provenance, verification checks, and support for direct, Slurm, and PBS backends.

Enables a single Antigravity IDE to explicitly drive multiple isolated Antigravity CLI workers, choosing each worker's account, model, skills, task, and expected outputs while the bridge executes those decisions over STDIO with SQLite-backed task state. It returns bounded logs, cancellable task control, and immutable artifact exports whose chunks and whole files can be verified by SHA-256.

Automates Ansys Fluent simulation workflows, enabling environment checks, case execution, UDF management, and result export through MCP tools.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

MCP server for overseer.nvim — gives any MCP-capable coding agent running inside Neovim (Claude Code, Codex, Gemini CLI, opencode) the same visibility and control over tasks the user has: list, run, tail, restart, stop. Agent-started dev servers land in the user's own task list instead of becoming invisible orphaned processes.

A persistent Python REPL MCP server for AI agents with stateful execution, real timeouts, crash isolation, and an MCP bridge to call other tools in the project.

A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.

Provides a set of tools to interact with Tecton clusters, manage feature stores, and execute Tecton CLI commands through the Mission Control Protocol.

Provides blocking await tools to poll commands, URLs, or files until a condition is met, eliminating the need for sleep loops in agent workflows.

Provides the current date in multiple formats (e.g., European, ISO, US) via a simple MCP tool.

An MCP server that lets AI agents list sessions, read terminal screens, type commands, and run commands (with optional waiting for exit status) in Rex terminal blocks on the local machine through the rex CLI. It exposes a single allowlisted passthrough CLI tool plus a guide tool, deliberately excluding destructive or cross-host operations.

Cognitive security for AI-assisted development, preventing dangerous commands and large AI code rewrites when fatigued.

Enables handing one self-contained job to the Claude Code CLI on the local machine and getting back its final message plus a manifest of the artifacts it produced, without letting Claude write into the user's project. Reads are limited to named directories and all writes stay in a throwaway run directory, so the user reviews the listed files and copies in only what they approve.

Enables MCP clients to safely read, write, edit, search, and optionally run commands within one or more named project workspaces, while maintaining a work journal and tracking changes.

Enables AI assistants to manage remote servers via SSH with 14 commands for execution, file transfer, auditing, and monitoring.

Enables creating temporary Quarto projects, writing files into them, rendering documents, and inspecting project output over MCP, while managing reusable metadata defaults.

Enables secure file system operations (read, write, delete) and simulated command execution with server-enforced permission policies, risk assessment, and human-in-the-loop approval.

Delegates coding and read-only exploration tasks to the Antigravity CLI as blocking headless runs, mirroring live progress to on-disk stream transcripts and diffing git status to report touched files and out-of-scope edits. Supports resumable conversations, per-call model/agent/effort overrides, idle and hard timeouts, and discovery of available models and agent profiles.

An MCP server that provides sandboxed shell command execution with configurable security policies, enabling safe AI-assisted command runs within a project repository.

Runs shell commands inside an existing microsandbox sandbox rather than on the Windows host, tracking the working directory and translating paths between the host project folder and the sandbox mount. Exposes tools for foreground or background execution with timeouts, output retrieval, killing processes, and listing them.