mcp2cli

Convert MCP servers into CLI commands and SKILLs.

Also known as: mcp2skill · mcp-to-cli · mcp-to-skill · mcpcli

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  │ <──────────────────  │ (gitlab)   │
  └─────────┘     JSON result      └────────────┘

The daemon starts automatically on first use and stops when idle.

Presets — Pre-built Skills, Download in Seconds

Don't want to wait for AI generation (~2-3 min)? Use presets — pre-built CLI mappings + skill files shared by the community. Downloads in ~10 seconds.

mcp2cli preset list                       # Browse available presets
mcp2cli preset pull mcp-atlassian         # Download + sync to AI clients
mcp2cli preset pull mcp-atlassian@1.2.3   # Pin a specific version
mcp2cli preset push gitlab-mcp            # Share your result with others

During convert and install, presets are checked automatically — if one exists, you can skip AI generation entirely:

mcp2cli convert/install
        |
   preset-check ─── found? ─── yes ──→ download preset (~10s)
        |                                      |
        no                                     |
        |                                      |
   scan → generate cli → generate skill        |
        (~2-3 min)                             |
        |                                      |
        └──────────────→ skill sync ←──────────┘

Custom registry for private/team use:

mcp2cli preset registry set https://github.com/your-org/mcp2cli-presets

Install — Add New MCP Servers

convert works with servers already configured in your AI clients. For MCP servers you haven't set up yet, use install — it searches the internet for installation info, prompts for env vars, then runs the full conversion pipeline:

mcp2cli install mcp-atlassian

Usage Examples

# List all configured local MCP servers
mcp2cli list

# Convert a server (auto-detects config from all clients)
mcp2cli convert gitlab-mcp

# Use the generated CLI
mcp2cli gitlab mr list --project-id 123
mcp2cli gitlab issue create --title "Fix login bug" --project-id 123

# Update when the MCP server adds new tools
mcp2cli update gitlab-mcp

# Remove everything (CLI, skills, config)
mcp2cli remove gitlab-mcp

Commands

List Exist MCP servers

Command	When to use
mcp2cli list	Use this to see which MCP servers mcp2cli can currently discover before converting or updating anything.

Core workflows

Command	When to use
mcp2cli convert <server>	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 <server>	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 <server>	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 <server>	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 <name[@version]>	Use this when a preset already exists and you want the CLI + skill files immediately without waiting for scan/generation.
mcp2cli preset export <server>	Use this to package your local generated files into a reusable preset bundle.
mcp2cli preset push <server>	Use this when you want to publish a local preset to the shared preset registry.

License

MIT