"Getting Help with GitHub Actions Workflow Coding" matching MCP servers:

Experimental MCP server for local LLM orchestration with filesystem tools (read, write, list, delete files) and a CLI agent that communicates via Ollama.

Provides coding agent capabilities including file operations, terminal commands, search functionality, and utility operations.

An MCP execution layer that lets capable AI models safely control a developer workstation for file edits, terminal and process execution, Git operations, language-server analysis, browser and Windows UI automation, media processing, job/resource control, and checkpointing. It also coordinates durable single- or multi-agent tasks across providers with leases, dependency graphs, evidence logs, repair loops, and separate verification gates.

A transport-agnostic MCP server that integrates multiple AI coding agents (Claude Code, Gemini, and Codex) with built-in tools for command execution, calculations, and streaming capabilities. Supports both STDIO and HTTP transports for flexible deployment.

A Rust + Tauri 2 desktop application that turns local project folders into persistent MCP workspaces, enabling AI agents to read files, modify code, run commands and tests, check Git status, and maintain cross-conversation development context through historical checkpoints, with built-in WSS/FRP/Cloudflare tunnels and OAuth for connecting to ChatGPT.

A local MCP server that gives AI coding assistants read, write, search, and execute capabilities within a specified project directory. This Windows GUI build simplifies setup by eliminating manual Python, Node.js, or Docker installation.

Enables AI agents to develop within a local project workspace by reading and modifying files, running commands and tests, checking Git state, and persisting progress as history sessions that can be restored in later conversations.

Enables ChatGPT web to interact with local Windows/WSL shell and code workspaces via an MCP server, providing file access, shell execution, and snapshot-based workspace management with per-command authorization.

Provides 29 Git operations and 11 workflow combinations for AI assistants and developers, enabling comprehensive Git repository management through a standardized interface.

Wraps GitHub Copilot CLI as an MCP server, enabling MCP clients to run Copilot conversations for code tasks with support for session resumption and permission modes.

Enables ChatGPT to inspect selected local projects, make edits, run commands/tests, and review diffs through an authenticated MCP endpoint backed by a containerized workspace.

Acts as a unified dispatch hub for local coding agent CLIs, supporting capability-based routing, schema validation, and retries via MCP tools.

Enables AI agents to turn local projects into persistent workspaces, providing file editing, command execution, Git integration, and cross-session history management.

Wraps the GitHub Copilot CLI to provide natural language command suggestions, explanations, and shell configuration for shell, git, and GitHub CLI commands through the Model Context Protocol.

Enables remote server control via SSH through Claude, supporting command execution, file transfers, and multi-server management with both standard and streamable HTTP protocols.

Enables executing Python code, managing environments and packages, and performing file operations within Docker containers.

Enables querying today's GitHub commits, performing local git operations (status, diff, add, commit, push), and creating GitHub issues through natural language.

Converts any Python Click or Typer CLI into an MCP server automatically — zero-code transformation that introspects CLI commands and exposes them as MCP tools.

Enables AI-assisted development tools like Cursor, Cline, and Windsurf to request user confirmation before acting, running shell commands and displaying their output inside a Qt feedback window that returns user notes and logs to the model. This human-in-the-loop loop lets the assistant consolidate many speculative tool calls into a single feedback-aware request, saving API usage and improving accuracy.

Enables AI clients to operate on a remote Linux server over MCP, allowing shell command execution, file editing, package installation, project builds, and service management.