Mcp2cli

mcp2cli/mcp2skill

> Also known as: mcp2skill · mcp-to-cli · mcp-to-skill · mcpcli Convert MCP servers into CLI and Agent SKILLs.

Why

For example, install zereight/gitlab-mcp — it exposes 122 tools across 14 categories (merge requests, issues, pipelines, wikis, etc.). Every tool schema gets injected into your AI conversation context on every request. We measured this directly against the Claude API:

| Per-request input tokens | Before (raw MCP) | After (mcp2cli) | |---|---|---| | Your message | 20 | 20 | | Tool definitions (122 tools) | 27,969 | — | | Skill file (1 file) | — | ~800 | | Total | 27,989 | ~820 | | Reduction | | ~97% |

After mcp2cli convert, your AI agent stops seeing 122 tool definitions. Instead, it reads one small skill file, and calls CLI commands like mcp2cli gitlab mr create --project-id 123 --title "Fix bug" to get the job done.

Beyond token savings, some projects maintain both a CLI and an MCP server in parallel (e.g. the GitLab MCP server vs. the glab CLI). Keeping two interfaces in sync is a maintenance burden and inevitably leads to feature drift. mcp2cli eliminates the need — one MCP server, one generated CLI, always consistent.

Quick Start

pip install mcp-to-cli

If you already have an MCP server configured in Claude, Cursor, or Codex:

mcp2cli list              # See which MCP servers are available
mcp2cli convert gitlab-mcp

That's it. Your MCP server is now a CLI, and the compressed skill files are synced to your AI clients.

> Tip: After install or convert, you can edit ~/.agents/mcp2cli/servers.yaml to update env vars (API keys, endpoints, etc.) without re-running the full setup.

How It Works

mcp2cli convert gitlab-mcp runs a 3-phase pipeline:

┌──────────────┐      ┌──────────────┐      ┌──────────────┐
│  1. Extract  │─────>│  2. Generate │─────>│   3. Sync    │
│              │      │              │      │              │
│  Read config │      │ AI-generate  │      │ Copy skills, │
│  from Claude,│      │ CLI + skill  │      │ disable MCP  │
│  Cursor,Codex│      │              │      │              │
└──────┬───────┘      └──────┬───────┘      └──────┬───────┘
       v                     v                     v
 mcp_servers.json    cli.yaml + SKILL.md    ~/.claude/skills/

1. Extract — Reads your MCP server config from Claude (~/.claude.json) / Cursor (~/.cursor/mcp.json) / Codex (~/.codex/config.toml)
2. Generate — Connects to the MCP server, discovers all tools, and uses AI to generate a CLI command tree (cli.yaml) and a compressed skill file (SKILL.md)
3. Sync — Copies skill files to your AI clients and disables the raw MCP server

Each skill directory contains a users/ subdirectory where you can add your own notes and workflows. This directory is never overwritten by mcp2cli.

At runtime, the generated CLI resolves commands to MCP tool calls through a lightweight daemon:

mcp2cli gitlab mr list --project-id 123
        │
        │  resolve via cli.yaml
        v
  tool: gitlab_list_merge_requests(project_id=123)
        │
        v
  ┌─────────┐     MCP protocol     ┌────────────┐
  │ mcp2cli │ ──────────────────>  │ MCP Server │
  │ daemon  │ ` | Use this when the MCP server is already configured in Claude, Cursor, or Codex and you want to convert it into a generated CLI + synced skills. |
| `mcp2cli install ` | Use this when the server is not exist yet and you want `mcp2cli` to install/register it, then generate or pull the CLI + skills for you. |
| `mcp2cli update ` | Use this after the MCP server adds, removes, or changes tools and you need to regenerate the local CLI mapping and skill files. |
| `mcp2cli uninstall ` | Use this when you want to remove generated CLI/skill artifacts for a server. |

### Presets
| Command | When to use |
|---|---|
| `mcp2cli preset list [server]` | Use this to check whether a pre-generated preset already exists before running AI generation. |
| `mcp2cli preset pull ` | Use this when a preset already exists and you want the CLI + skill files immediately without waiting for scan/generation. |
| `mcp2cli preset export ` | Use this to package your local generated files into a reusable preset bundle. |
| `mcp2cli preset push ` | Use this when you want to publish a local preset to the shared preset registry. |

## License

MIT

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [makabakaxy](https://github.com/makabakaxy)
- **Source:** [makabakaxy/mcp2cli](https://github.com/makabakaxy/mcp2cli)
- **License:** MIT

Install and usage instructions live in the source repository linked above.