MCP server: One MCP server.

gutsy

@kouhxpMCP server · External

Local gut check before risky agent actions: calibrated safe/intended probabilities, no API calls.

AI agent governancev0.1.2Apache-2.0Python
Freeno charge per call

Runs on your machine from its package; the steps are below.

What it does

  1. Reads
  2. Writes
  3. High impact

Not assessed yet.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Runs locally over stdio

This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).

PyPIgutsy-mcpv0.1.2

Run

uvx gutsy-mcp

MCP client config

{
  "mcpServers": {
    "gutsy": {
      "command": "uvx",
      "args": [
        "gutsy-mcp"
      ]
    }
  }
}
README2 min read

gutsy-mcp

A local gut check for coding agents. Before Claude Code, Codex, Cursor or Copilot runs rm -rf, force-pushes, deploys or sends something, it calls gut_check and gets back calibrated probabilities that the action is intended and safe, plus a recommendation: proceed, confirm (ask the user first) or block.

Runs on your CPU with gutsy (0.8B, 775 MB GGUF). No API calls, no per-call cost, deterministic, and your code never leaves the machine.

What it is and isn't

It's a fast second opinion that adds friction where it's warranted. It is not a security boundary: the agent decides whether to call it and writes the context it sees, and a 0.8B model is weak at ambiguity and multi-step rules (see the model card). Keep your normal permission settings on. Default thresholds aren't tuned for your workflow; log the probabilities and adjust them.

1. Start the gutsy runtime

bash
git clone https://github.com/kouhxp/gutsy
pip install -e gutsy/gutsy-inference
hf download kouhxp/gutsy --include "gutsy-0.8b-v04*" --local-dir gutsy/gutsy-inference/models
cd gutsy/gutsy-inference && cp models.example.json models.json
gutsy-inference check && gutsy-inference serve     # http://127.0.0.1:8765

Or set GUTSY_INFERENCE_DIR=/path/to/gutsy/gutsy-inference and gutsy-mcp starts it on first use.

2. Add the MCP server

Claude Code:

bash
claude mcp add gutsy -- uvx gutsy-mcp

Cursor (.cursor/mcp.json) / Claude Desktop:

json
{ "mcpServers": { "gutsy": { "command": "uvx", "args": ["gutsy-mcp"] } } }

VS Code (.vscode/mcp.json):

json
{ "servers": { "gutsy": { "type": "stdio", "command": "uvx", "args": ["gutsy-mcp"] } } }

Codex (~/.codex/config.toml):

toml
[mcp_servers.gutsy]
command = "uvx"
args = ["gutsy-mcp"]

Then tell the agent when to use it (CLAUDE.md, AGENTS.md, .cursor/rules):

Before deleting files, rewriting git history, force-pushing, touching databases, deploying, publishing or sending anything, call gut_check. If it doesn't return proceed, stop and ask me.

3. Optional: enforce it with a Claude Code hook

An MCP tool only runs if the agent remembers to call it. The hook runs on every risky-looking Bash command regardless. It can only ask or deny, never auto-approve.

bash
uv tool install gutsy-mcp      # puts gutsy-hook on PATH

.claude/settings.json:

json
{
  "hooks": {
    "PreToolUse": [
      { "matcher": "Bash", "hooks": [{ "type": "command", "command": "gutsy-hook", "timeout": 60 }] }
    ]
  }
}

block becomes a confirmation prompt by default; set GUTSY_HOOK_ALLOW_DENY=1 to deny outright.

Tool

gut_check(action, context="", user_request="") returns:

json
{
  "recommendation": "confirm",
  "reason": "Not confident enough to proceed without the user.",
  "p_intended": 0.41,
  "p_reversible": 0.08,
  "verdict_probabilities": {"proceed": 0.22, "confirm": 0.61, "block": 0.17},
  "reject": 0.04,
  "confidence": 0.42
}
env vardefault
GUTSY_URLhttp://127.0.0.1:8765runtime URL
GUTSY_INFERENCE_DIRlets gutsy-mcp start the runtime
GUTSY_MODELruntime defaultmodel name from models.json
GUTSY_PROCEED_MIN0.85
GUTSY_BLOCK_MIN0.50
GUTSY_API_KEYif you run serve --api-key-env

License: Apache 2.0.

Similar modules

Browse AI agent governance