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Mcp2cli

Skill tdw419/ascii-world/apps/mcp2cli/skills/mcp2cli

Build, Control, and Render — AI agents create software from ASCII templates. The same ASCII becomes the running app AND can render as beautiful GUIs.

Install
npx -y skills add tdw419/ascii-world --skill mcp2cli

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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.

SKILL.md

5.9 KB, ~1.5k tokens by cl100k_base, as published. Nobody here has run it

mcp2cli

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

Install

# 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
# 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 (repeatable, value supports env:/file: prefixes)
  --base-url URL          Override base URL from spec
  --transport TYPE        MCP HTTP transport: auto|sse|streamable (default: auto)
  --env KEY=VALUE         Env var for stdio server process (repeatable)
  --oauth                 Enable OAuth (authorization code + PKCE flow)
  --oauth-client-id ID    OAuth client ID (supports env:/file: prefixes)
  --oauth-client-secret S OAuth client secret (supports env:/file: prefixes)
  --oauth-scope SCOPE     OAuth scope(s) to request
  --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

# API key header (literal value)
mcp2cli --spec ./spec.json --auth-header "Authorization:Bearer tok_..." list-items

# Secret from environment variable (avoids exposing in process list)
mcp2cli --mcp https://mcp.example.com/sse \
  --auth-header "Authorization:env:API_TOKEN" \
  search --query "test"

# Secret from file
mcp2cli --mcp https://mcp.example.com/sse \
  --auth-header "x-api-key:file:/run/secrets/api_key" \
  search --query "test"

OAuth authentication (MCP HTTP only)

# Authorization code + PKCE (opens browser)
mcp2cli --mcp https://mcp.example.com/sse --oauth --list

# Client credentials (machine-to-machine)
mcp2cli --mcp https://mcp.example.com/sse \
  --oauth-client-id "my-id" --oauth-client-secret "my-secret" \
  search --query "test"

# With scopes
mcp2cli --mcp https://mcp.example.com/sse --oauth --oauth-scope "read write" --list

Tokens are cached in ~/.cache/mcp2cli/oauth/ and refreshed automatically.

Transport selection (MCP HTTP only)

# Default: tries streamable HTTP, falls back to SSE
mcp2cli --mcp https://mcp.example.com/sse --list

# Force SSE transport (skip streamable HTTP attempt)
mcp2cli --mcp https://mcp.example.com/sse --transport sse --list

# Force streamable HTTP (no SSE fallback)
mcp2cli --mcp https://mcp.example.com/sse --transport streamable --list

POST with JSON body from stdin

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

Env vars for stdio servers

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.

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)

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:

    uvx mcp2cli --mcp https://target.example.com/sse --list

  2. Inspect each command to understand parameters:

    uvx mcp2cli --mcp https://target.example.com/sse <command> --help

  3. Test key commands to verify they work:

    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.

Gives 0 of the 12 instructions most mcp tooling skills give in ~1.5k tokens

Counted across 638 of the 750 authors here whose files we hold, read 2026-08-06

  • create ten complex read-only evaluation questionsin 71 of 638, across 17 files
  • test servers using MCP Inspectorin 60 of 638, across 18 files
  • provide actionable error messagesin 56 of 638, across 14 files
  • prioritize comprehensive API coverage over specific workflowsin 54 of 638, across 12 files
  • use TypeScript and Streamable HTTP for remote serversin 53 of 638, across 7 files
  • define structured output schemas where possiblein 51 of 638, across 9 files
  • use Zod or Pydantic for input schemasin 48 of 638, across 6 files
  • fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
  • load framework documentation using WebFetchin 45 of 638, across 3 files
  • verify each evaluation answer independentlyin 45 of 638, across 3 files
  • implement API client with authentication and paginationin 45 of 638, across 3 files
  • Define input schemas with validationin 28 of 638, across 10 files

Said here and by no other author read

  • connect to an MCP server or OpenAPI spec
  • discover available commands using --list
  • inspect specific commands using --help
  • execute commands with required flags
  • read JSON body from stdin
  • use --toon for large uniform arrays

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.