MCP.Market

gutsy MCP server

by kouhxp·io.github.kouhxp/gutsy·v0.1.2

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

A89/100grade A
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Safety scan
A89/100

full report

Adoption
Growing

0 stars379 downloads/wk

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If you have run it, two minutes of your experience saves the next person an afternoon.

gutsy tools (1)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • gut_check

    Get a local, calibrated second opinion BEFORE a risky or irreversible action.

Public scan report

scanner v0.1.10 · 2026-10-13 · same rubric, same numbers if you re-run it

no findings
  • Code scan6 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 6 days ago15/15
  • Maintainer identityregistry namespace matches repository owner6/10
Overall 89/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How gutsy compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
89/100
higher than 83% of 4,460 AI and agents servers
Tools
1
the typical AI and agents server has 6
Tools that change things
0%
typical: 0%. Fewer write tools means less an agent can break
Downloads a week
379
more than 83% of 1,538 AI and agents servers with a package

What its tools let an agent do: 1 read

Install gutsy in Claude Code, Cursor or VS Code

claude mcp add gutsy -- uvx gutsy-mcp
Add to Cursor

What the publisher says

From the gutsy repository's README, as published. We do not edit it. Read it on GitHub

gutsy-mcp

<!-- mcp-name: io.github.kouhxp/gutsy -->

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

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 GUTSYINFERENCEDIR=/path/to/gutsy/gutsy-inference and gutsy-mcp starts it on first use.

2. Add the MCP server

Claude Code:

claude mcp add gutsy -- uvx gutsy-mcp

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

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

VS Code (.vscode/mcp.json):

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

Codex (~/.codex/config.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.

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

.claude/settings.json:

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

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

Tool

gutcheck(action, context="", userrequest="") returns:

{
  "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
}

License: Apache 2.0.

Nothing above is checked by us. What we check is on the safety report.

gutsy: common questions

Is gutsy MCP server safe?
Yes, by our scan: it is graded A (89/100). Read the gutsy safety report
How do I install gutsy?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does gutsy need an API key?
No secret keys are declared. It reads 4 settings from the environment.
Is gutsy maintained?
The last commit was 28 days ago (2026-10-07). The latest release is v0.1.2.
What can I use instead of gutsy?
Servers from other publishers that do the same job: Decionis MCP server, Agent-Native Chat MCP server and Emilia Protocol MCP server.
How does gutsy compare with other AI and agents MCP servers?
safety score 89/100 (higher than 83% of 4,460 AI and agents servers); tools 1 (the typical AI and agents server has 6); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to gutsy

Same job from other publishers: the closest match first, then the best rated.

  • Decionis
    Authorize consequential AI agent actions before execution
    A
  • Agent-Native Chat
    Minimal chat-first app with durable threads, actions, and the app-agent loop
    A
  • Emilia Protocol
    Exact-action approval for consequential agent actions: request, track, and verify signed receipts.
    B
  • Kiln
    Spawn an object into existence: describe it, and your agent designs it, checks it, and 3D prints it.
    B
  • three.ws Provenance
    Append-only, signed, on-chain-verifiable agent action log — record and audit what agents did.
    B