Scmcp MCP Server

You can use scmcp in most AI clients, plugins, or agent frameworks that support the MCP:. That is what the scmcp mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.

The tools it exposes

The server publishes 1 tool. What each one is for:

• Usage — Configure MCP client: json { "mcpServers": { "scmcp": { "command": "/home/test/bin/scmcp", "args": ["run", "--run-mode", "tool"] } } }

Getting it running

The server ships on PyPI as scmcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.

How it compares

Among the developer tooling options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Scmcp's toolset — Usage — is a fair guide to whether it matches your workflow. It is maintained by scmcphub; worth a glance at recent repository activity before you build anything load-bearing on it.

This entry was verified against Scmcp's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.

Things to watch

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.