mcp-to-cli 0.1.1

Convert MCP server tools into hierarchical CLI commands and agent Skills

mcp2cli

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

Convert MCP servers into CLI commands and compact AI 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.

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

Command	Description
convert <server>	Convert an MCP server to skill-based usage
install <server>	Install a new MCP server and generate skills
update <server>	Update tools and regenerate CLI + skills
remove <server>	Remove a server and all generated artifacts
list	List all configured MCP servers
scan <server>	Discover all tools from an MCP server
tools <server>	List tools or show tool details
call <server> <tool>	Call an MCP tool directly
generate cli <server>	(Re)generate the CLI command tree
generate skill <server>	(Re)generate skill files
skill sync <server>	Sync skill files to AI clients
skill unsync <server>	Remove synced skills and re-enable MCP server
preset list [server]	Browse available presets (local + remote)
preset pull <name[@ver]>	Download a preset, skip AI generation
preset export <server>	Export a preset bundle to a local directory
preset push <server>	Push local preset to community registry
validate <server>	Validate CLI mapping and skill files

License

MIT