mcp2cli

Original：🇺🇸 English
Translated

Turn any MCP server or OpenAPI spec into a CLI. Use this skill when the user wants to interact with an MCP server or OpenAPI/REST API via command line, discover available tools/endpoints, call API operations, or generate a new skill from an API. Triggers include "mcp2cli", "call this MCP server", "use this API", "list tools from", "create a skill for this API", or any task involving MCP tool invocation or OpenAPI endpoint calls without writing code.

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Sourceknowsuchagency/mcp2cli
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NPX Install

npx skill4agent add knowsuchagency/mcp2cli mcp2cli

Tags

Translated version includes tags in frontmatter
mcp2cliopenapi-to-climcp-server-integrationapi-cli-toolskill-generation

SKILL.md Content

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mcp2cli

Turn any MCP server or OpenAPI spec into a CLI at runtime. No codegen.

Install

bash
# Run directly (no install needed)
uvx mcp2cli --help

# Or install
pip install mcp2cli

Core Workflow

  1. Connect to a source (MCP server or OpenAPI spec)
  2. Discover available commands with 
    --list
  3. Inspect a specific command with 
    <command> --help
  4. Execute the command with flags
bash
# MCP over HTTP
mcp2cli --mcp https://mcp.example.com/sse --list
mcp2cli --mcp https://mcp.example.com/sse create-task --help
mcp2cli --mcp https://mcp.example.com/sse create-task --title "Fix bug"

# MCP over stdio
mcp2cli --mcp-stdio "npx @modelcontextprotocol/server-filesystem /tmp" --list
mcp2cli --mcp-stdio "npx @modelcontextprotocol/server-filesystem /tmp" read-file --path /tmp/hello.txt

# OpenAPI spec (remote or local, JSON or YAML)
mcp2cli --spec https://petstore3.swagger.io/api/v3/openapi.json --list
mcp2cli --spec ./openapi.json --base-url https://api.example.com list-pets --status available

CLI Reference

mcp2cli [global options] <subcommand> [command options]

Source (mutually exclusive, one required):
  --spec URL|FILE       OpenAPI spec (JSON or YAML, local or remote)
  --mcp URL             MCP server URL (HTTP/SSE)
  --mcp-stdio CMD       MCP server command (stdio transport)

Options:
  --auth-header K:V     HTTP header sent with requests (repeatable)
  --base-url URL        Override base URL from spec
  --env KEY=VALUE       Env var for stdio server process (repeatable)
  --cache-key KEY       Custom cache key
  --cache-ttl SECONDS   Cache TTL (default: 3600)
  --refresh             Bypass cache
  --list                List available subcommands
  --pretty              Pretty-print JSON output
  --raw                 Print raw response body
  --toon                Encode output as TOON (token-efficient for LLMs)
  --version             Show version
Subcommands and flags are generated dynamically from the source.

Patterns

Authentication

bash
# API key header
mcp2cli --spec ./spec.json --auth-header "Authorization:Bearer tok_..." list-items

# Multiple headers
mcp2cli --mcp https://mcp.example.com/sse \
  --auth-header "x-api-key:sk-..." \
  --auth-header "x-org-id:org_123" \
  search --query "test"

POST with JSON body from stdin

bash
echo '{"name": "Fido", "tag": "dog"}' | mcp2cli --spec ./spec.json create-pet --stdin

Env vars for stdio servers

bash
mcp2cli --mcp-stdio "node server.js" --env API_KEY=sk-... --env DEBUG=1 search --query "test"

Caching

Specs and MCP tool lists are cached in 
~/.cache/mcp2cli/
(1h TTL). Local files are never cached.
bash
mcp2cli --spec https://api.example.com/spec.json --refresh --list    # Force refresh
mcp2cli --spec https://api.example.com/spec.json --cache-ttl 86400 --list  # 24h TTL

TOON output (token-efficient for LLMs)

bash
mcp2cli --mcp https://mcp.example.com/sse --toon list-tags
Best for large uniform arrays — 40-60% fewer tokens than JSON.

Generating a Skill from an API

When the user asks to create a skill from an MCP server or OpenAPI spec, follow this workflow:
  1. Discover all available commands:
    bash
    uvx mcp2cli --mcp https://target.example.com/sse --list
  2. Inspect each command to understand parameters:
    bash
    uvx mcp2cli --mcp https://target.example.com/sse <command> --help
  3. Test key commands to verify they work:
    bash
    uvx mcp2cli --mcp https://target.example.com/sse <command> --param value
  4. Create a SKILL.md that teaches another AI agent how to use this API via mcp2cli. Include:
    • The source flag (
      --mcp
      , 
      --mcp-stdio
      , or 
      --spec
      ) and URL
    • Any required auth headers
    • Common workflows with example commands
    • The 
      --list
      and 
      --help
      discovery pattern for commands not covered
The generated skill should use mcp2cli as its execution layer — the agent runs 
uvx mcp2cli ...
commands rather than making raw HTTP/MCP calls.