"A search for the term 'terminal'" matching MCP servers:

Provides brutally honest code reviews, explanations, and refactoring suggestions via CLI and MCP tools.

An MCP server that allows coding agents to search the Swift Package Index and retrieve GitHub READMEs without requiring an API key. It features comprehensive search filters for stars, platforms, and licenses, providing token-efficient responses optimized for LLM comprehension.

Long-term memory for Claude Desktop and Claude Code over plain markdown files on your machine. It returns nothing when the corpus has no answer, naming the terms that appear nowhere — no least-bad match dressed as an answer. And it verifies cited commit SHAs against git, so an agent can tell what actually shipped from what was only discussed and abandoned. Local; nothing is transmitted.

Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.

An MCP server that connects Claude Desktop to an interactive Claude Code session running in a tmux terminal.

Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.

Prevents coding agents from repeatedly attempting the same failed fix by tracking attempts and blocking further fixes until the agent uses its own web search tool.

Enables Codex to delegate review and implementation tasks to the local Qoder CN CLI as background jobs, with two permission tiers and polling-based status updates.

Enables Windows users to connect Antigravity CLI as an MCP subagent for coding tasks such as file reading, code execution, edits, and terminal commands, with model selection, conversation continuity, and streaming progress.

An MCP server that enables local AI models to receive guidance from remote 'senior' AI providers like OpenAI, Anthropic, and Gemini to solve programming problems. It features intelligent multi-turn dialogue management, context synchronization, and automated session history tracking.

Drive Elixir terminal UIs headlessly from an agent: start a TUI session, send keystrokes, take structured screenshots, and read TEA model state as MCP resources. Also exposes adaptive-UI recommendation tools and, when raxol_agent is loaded, read-only coding-agent session tools.

Enables primary AI orchestrators to delegate token-heavy coding tasks to economical models via OpenRouter, running sandboxed autonomous ReAct loops with filesystem operations and git audit tools while reducing token costs.

Exposes the local Claude Code CLI as a subagent for Codex, enabling background review and implementation jobs with two fixed permission tiers.

Connects AI coding agents to a shared knowledge base by allowing them to search, retrieve, and contribute coding traces. It acts as a protocol adapter for the CommonTrace API, facilitating collaborative knowledge sharing through natural language queries and tool-based interactions.

Enables AI agents to delegate complex coding tasks, refactorings, and automated code reviews to OpenAI Codex and Google Antigravity CLIs, running them autonomously in YOLO mode with process supervision.

Delegates work from MCP clients (like Claude Code) to the Codex CLI, allowing spawning of autonomous Codex subagents for tasks.

An MCP (Model Context Protocol) server that enables integration with the Simplifier Low Code Platform. This server provides tools and capabilities for creating and managing Simplifier Connectors and BusinessObjects through the platform's REST API.

Enables a supervisor agent to create, coordinate, steer, inspect, and interrupt multiple Codex agents as a subagent pool within an MCP host, running them alongside the host's own subagents.

Enables running multiple AI CLI tools (Claude, Codex, Gemini, Forge, OpenCode) as background processes with automatic permission handling, allowing LLMs to execute async multitasking and combine different AI models.

A cross-platform MCP server implementing the Ralph Loop iterative development technique where a worker model does the work and a reviewer model provides cross-model review until approval.