MCP Skill Server MCP Server

Most coding assistants now support skills natively, so an MCP server just for skill discovery isn't necessary. Where this package adds value is making skills' execution deterministic and deployable — with a fixed entry point and controlled execution, skills developed in your editor can run in non-sandboxed production environments. It also supports incremental loading, so agents discover skills on demand instead of loading everything upfront.

MCP Skill Server

Build agent skills where you work. Write a Python script, add a SKILL.md, and your agent can use it immediately. Iterate in real-time as part of your daily workflow. When it's ready, deploy the same skill to production — no rewrite needed.

Why?

Most skill development looks like this: write code → deploy → test in a staging agent → realize it's wrong → redeploy → repeat. It's slow, and you never get to actually use the skill while building it.

MCP Skill Server flips this. It runs on your machine, inside your editor — Claude Code, Cursor, or Claude Desktop. You develop a skill and use it in your real work at the same time. That tight feedback loop (edit → save → use) means you discover what's missing naturally, not through artificial test scenarios. The premise is if the skill doesn't work well with Claude Code, it's unlikely to work with a less sophisticated agent.

How skills mature to survive in the outside world

Claude skills can already have companion scripts, but there's no formalized entry point — the agent decides how to invoke them. That works for local use, but it's not deployable: a production MCP server can't reliably call a skill if the execution path isn't fixed.

MCP Skill Server enforces a declared entry field in your SKILL.md frontmatter (e.g. entry: uv run python my_script.py). This gives you a single, fixed entry point that the server controls. Commands and parameters are discovered from the script's --help output — that's the source of truth, not the LLM's interpretation of your code.

1. Claude/coding agent skill                → SKILL.md + scripts, but no fixed entry — agent decides how to run them
2. Local MCP skill (+ entry)   → Fixed entry point, schema from --help, usable daily via this server
3. Production                  → Same skill, same entry — deployed to your enterprise MCP server

Sharpen locally, then harden for production

Every agent that connects to the MCP server gets the same interface — list_skills, get_skill, run_skill — so the skill's description, parameter names, and help text are identical regardless of which agent calls them. That said, different agents have different strengths — a skill that works locally still needs testing with your production agent.

1. Use it yourself — build the skill, use it daily via Claude Code or Cursor. Fix descriptions and param names when the agent misuses the skill.
2. Test with a weaker model — try a smaller model to surface interface ambiguity.
3. Add a deterministic entry point — declare entry in SKILL.md for reliable, secure execution. Use skill init to scaffold it, skill validate to check readiness.
4. Test with your production agent — verify end-to-end in your target environment, then deploy.

Install

Claude Desktop (one-click)

After installing, edit the skills path in your Claude Desktop config to point to your skills directory.

Claude Code

claude mcp add skills -- uvx mcp-skill-server serve /path/to/my/skills

Cursor

Add to .cursor/mcp.json in your project (or Settings → MCP → Add Server):

{
  "mcpServers": {
    "skills": {
      "command": "uvx",
      "args": ["mcp-skill-server", "serve", "/path/to/my/skills"]
    }
  }
}

Manual install

# From PyPI (recommended)
uv pip install mcp-skill-server

# Or from source
git clone https://github.com/jcc-ne/mcp-skill-server
cd mcp-skill-server && uv sync

# Run the server
uvx mcp-skill-server serve /path/to/my/skills

Then add to your editor's MCP config:

{
  "mcpServers": {
    "skills": {
      "command": "uvx",
      "args": ["mcp-skill-server", "serve", "/path/to/my/skills"