defluff MCP server

defluff

The deterministic slop check for AI-generated prose. Point it at a changelog, a doc, or an agent's own output and get back the filler phrases to cut — plus a CI exit code and a pinnable score, identical on every run. No model, no API key.

Every flagged span carries no information, so cutting them loses nothing. Clean text, same tool, passes straight through:

What makes defluff worth installing over a one-off grep is the engine around the list: bring your own phrases, per-project overlays, and an MCP server your agents pick up with no wiring.

Install

pip install defluff

Or on macOS/Linux via Homebrew:

brew install ahmedak/defluff/defluff

That's it. No model download. No API key. Runs anywhere Python does.

Quick start

# Lint a file — exit 1 on slop, 0 when clean
defluff lint essay.md

# Pipe text
cat draft.md | defluff lint

# Get a bare score for scripts (0.0 – 1.0)
defluff score essay.md

# Machine-readable JSON for downstream tooling
defluff lint essay.md --json

MCP server

Exposes three tools so any MCP-aware agent can self-check prose without bespoke wiring — including its own draft, before returning it.

Zero-install via uvx (recommended) — pulls the package and the mcp extra on first run:

{
  "mcpServers": {
    "defluff": {
      "command": "uvx",
      "args": ["--from", "defluff[mcp]", "defluff-mcp"]
    }
  }
}

Or install it and run the entry point directly:

pip install "defluff[mcp]"
defluff-mcp

{
  "mcpServers": {
    "defluff": { "command": "defluff-mcp" }
  }
}

Published to the MCP Registry as io.github.ahmedak/defluff (see server.json).

<!-- ownership marker for the MCP Registry; do not remove --> mcp-name: io.github.ahmedak/defluff

Common use cases

• Agent self-correction — call slop_detect on a draft and revise the flagged phrases before returning it. Zero wiring, one session, no second model in the loop.
• CI gate on generated content — fail the build when an AI-drafted changelog or doc ships full of "furthermore" and "robust." Deterministic + exit codes + a pinnable lexicon is what an LLM-judge gate can't give you.
• Writing assistant feedback — highlight the exact phrases an editor would cut, instead of a vague "this sounds AI."
• A reward component for fine-tuning — see reward loops for caveats; on its own it's gameable.

Why defluff?

Shortened. The full README is on GitHub.