--- layout: tap site_name: glama tap_name: submit description: "Submit an MCP server to Glama directory" intent: write columns: - status - url args: - name: name type: string description: "Server name (e.g. 'Tap')" - name: repoUrl type: string description: "GitHub repository URL" args_json: | {"name":{"type":"string","description":"Server name (e.g. 'Tap')"},"repoUrl":{"type":"string","description":"GitHub repository URL"}} health_json: | {"min_rows":1,"non_empty":[]} example_args: "" source_url: https://github.com/LeonTing1010/tap-skills/blob/main/community/glama/submit.plan.json license: MIT ---

What it does

Submit an MCP server to Glama directory

Install Taprun once

Taprun ships as a single MCP server exposing a catalog of compiled taps. One-time setup on macOS / Linux:

brew install LeonTing1010/tap/taprun
tap mcp connect

Or drop this into your claude_desktop_config.json (works identically in Claude Code, Cursor, Cline, Windsurf — any MCP host):

{
  "mcpServers": {
    "tap": {
      "command": "tap",
      "args": ["mcp", "start"]
    }
  }
}

Call glama/submit

Terminal, once installed:

tap run glama/submit

From the MCP host — exact same compiled plan, deterministic replay, zero LLM tokens:

tap.run({ site: "glama", name: "submit" })

Why compile it once

This plan was forged once — the AI read glama, picked stable structural addresses (JSON-LD, ARIA, RSS, or declared API endpoints, in that priority order), and saved them to a .plan.json. Every replay since then has used zero LLM tokens. When glama ships a site change that breaks the extraction, tap verify surfaces it before your data goes stale — not after your pipeline silently writes garbage for a week.