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opendrs

by error-pathversion 0.5.0i★ –iRuns on your computer
Registry nameio.github.error-path/opendrs

Install

Overview

Description from the maker (original English)

PR blast radius and 0-1 risk score from git history, so low-risk PRs can be auto-approved.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "opendrs": {
      "command": "npx",
      "args": [
        "-y",
        "@error-path/opendrs",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add opendrs -- npx -y @error-path/opendrs mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "opendrs": {
      "command": "npx",
      "args": [
        "-y",
        "@error-path/opendrs",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "opendrs": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@error-path/opendrs",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account error-path, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

  • More stars than 0% of the other 1388 servers in this category
  • Published to the registry more recently than 75% of the other 1388 servers in this category
  • Basic checks equal to or better than 100% of the other 1388 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is opendrs safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.5.0) reached the registry 15 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

opendrs: PR blast radius analyzer and risk score for safe auto-approval

A calibrated 0-1 risk score for every pull request, so AI-reviewed, low-blast-radius PRs can be auto-approved into the merge queue while risky ones still wait for a human.

AI agents now write and review code faster than people can read it, so human review has become the bottleneck. Most pull requests are low risk, and a few cause most incidents. opendrs measures each PR's blast radius and risk: what the change touches, how many files depend on it, the history of the files it changes, author experience, missing tests and critical paths. It turns that into a 0-1 score using signals from your own git history. A team then sets a policy. For example, a PR that scores below 0.3 and has passed an AI review (Claude, CodeRabbit, Copilot or any check run) is approved and sent to the merge queue or auto-merge. Riskier PRs still wait for a human. The approach follows Meta's Diff Risk Score (DRS); opendrs is an independent, open-source implementation that runs in any git repository, with no service to host.

Contents: Install · Quick start · How it works · Features · Results · CLI and MCP · Documentation · Limitations · Compared with other tools · Prior art

Install

Use it asCommandDetails
GitHub Actionuses: error-path/opendrs@v0 (Marketplace)Comments, labels and, optionally, approves and merges. GitHub Action reference
CLInpx @error-path/opendrs scoreCLI, MCP server, Claude Code plugin and agent skill
MCP server (any agent)npx -y @error-path/opendrs mcpTools: score_diff, explain_file_risk, list_hotspots, suggest_reviewers
Claude Code plugin (skill plus MCP server)/plugin marketplace add error-path/opendrs, then /plugin install opendrs@opendrs
Agent skill (Claude Code, Cursor, Codex and others)npx skills add error-path/opendrsskills/opendrs/SKILL.md
Docker (MCP server over stdio)docker build -t opendrs . && docker run -i --rm -v "$PWD:/repo" opendrsMount the repository to score at /repo

Website and guides: error-path.github.io/opendrs.

Quick start

Score your current branch against the default branch. No configuration or API key is needed:

npx @error-path/opendrs score

You get a score, a tier (green, yellow or red), the signals that drove it with their evidence, and the policy decision. --json prints the full result, and --fail-above 0.6 makes the command exit 1 above a threshold.

To score every pull request, add .github/workflows/opendrs.yml (or run npx @error-path/opendrs init):

name: opendrs

on: pull_request: types: [opened, synchronize, reopened, ready_for_review, labeled, unlabeled] permissions: contents: read pull-requests: write checks: read jobs: opendrs: runs-on: ubuntu-latest steps:

  • uses: actions/checkout@v5 with: fetch-depth: 0 # required: the score reads past commits and authors
  • uses: error-path/opendrs@v0

This only comments and labels. Approval is off until you turn it on in .opendrs.yml on your default branch; see how to auto-approve low-risk PRs and the GitHub Action reference.

How it works

flowchart LR

diff["git diff merge-base...head"] --> signals hist["git history"] --> signals imports["import graph"] --> signals llm["LLM judge (optional)"] --> signals signals["Signals each with evidence and provenance"] --> model["Logistic model score = sigmoid(b + Σ w·x)"] model --> tier["Tier green / yellow / red"] tier --> policy["Policy blocks, size cap, unknowns, AI review, sampling"] policy --> decision["auto-approve deferred-review waiting-ai-review human-review"]

  1. Signals. Each signal provider reads the diff, the history and the import graph and emits features: size, how spread out the change is, past fixes and reverts in the touched files, the author's experience with them, complexity, blast radius (transitive dependents), critical paths and missing tests. Every signal records how it was obtained (observed, derived, inferred or unknown) and the evidence behind it. All signals
  2. Model. A logistic regression turns the features into a probability. Each signal's contribution w·x is reported, so you can see why a PR scored what it did. Out of the box the weights are hand-set priors; opendrs calibrate fits them to your repository. Calibration and backtesting
  3. Tiers. The score maps to green, yellow or red, mirroring Meta's gates. Thresholds can be absolute scores or percentiles of your own history.
  4. Policy. Ordered rules turn the score into a decision. Policy and safety rails

Where opendrs sits in your PR flow

A PR now often gets two rounds of AI review before a human looks at it. Those rounds catch local defects: typos, missing null checks, an obvious off-by-one. By the time a human opens the PR, that work is done, and what is left is a design and architecture judgment call made with less context than before. opendrs is built for that moment: it tells the final human how much attention the PR needs and names up to five architecture and history facts — a new import between modules, a broken public contract, a file that usually changes with one the PR didn't touch — that neither the editor nor the AI review comments showed. See the captain brief.

Features

FeatureWhat it doesDefaultDocs
Risk signalsSize, spread, file history, author experience, complexity, blast radius, critical paths, tests, languageonSignals
Calibration and backtestFit the weights to your history and measure capture at 5 / 10 / 50 % gatedrun on demandCalibration
Hard blocks and safety railsBlocked paths, binaries, submodules, self-modification guard, sampling, stale-approval checksonPolicy
Auto-approvalApprove green PRs after your AI review passes, then auto-merge or the merge queueoffAuto-approve guide
LLM judgeClassifies every hunk into safe or risk categories; can only raise the scoreoffLLM judge
requireSafe gateAuto-approve only when the judge calls every hunk safe with enough confidenceoffLLM judge
Per-source policyHuman, bot or AI-agent PRs get their own stricter green, daily cap and track recordoffPolicy
Deferred reviewA PR just above green lands now and gets a post-merge reviewoffPolicy
Landing delayAn eligible PR waits before approval, so a human can vetooffPolicy
Freeze windowsSuspend or tighten auto-approval on weekends or peak datesoffPolicy
Content blocklistRegexes on added lines send a PR to a humanoffConfiguration
Reviewer assignmentOne of the top three experts is asked to review risky PRson in the ActionPolicy
HotspotsRank files by risk at restrun on demandCLI and MCP
Review briefRanks hunks to read first, fixed check questions, safe-to-skim list, hard-rule reasons — recipes for claude-code-action, Bugbot, CodeRabbit (limited), Copilot and Codex (paste)onLLM reviewers, CLI and MCP
Pre-PR self-checkLists the ids an author agent should fix or explain before opening the PR; opendrs reads the verdicts back from the head commit and adds one advisory line, never part of the gateoffSelf-check recipe
Verification passOne batched LLM call re-checks every captain fact, review-first hunk and check with fresh context and keeps, drops or downgrades each one; never changes the score, tier or policyoffVerification pass
Incident feedbackopendrs feedback records incidents for calibrationrun on demandCalibration

Every setting lives in .opendrs.yml, read from the base branch so a PR cannot loosen its own gate: configuration reference.

Results

BenchmarkResultDetails
ApacheJIT (15 Apache Java projects, official 2017–2019 test years, apache/hadoop left out because it has no positive labels)Calibrated opendrs: AUC 0.823; catches 67 % of buggy commits in its riskiest 30 %, on par with a regression on the classic JIT metrics (0.821, 66 %)Results on ApacheJIT
ApacheJIT with the LLM judge (Claude Sonnet 5, 800 sampled test commits)At confidence 8, the commits the judge calls all-safe are buggy at 5.3 %, against a 25.5 % base rateWith the structured LLM judge
27 public repositories (calibrate, then backtest)Calibration beat the default priors on held-out commits in all nine repositories of the first runBenchmark results

CLI and MCP

CommandWhat it does
opendrs scoreScore merge-base(base, head)..head
opendrs explain A file's history, fixes, authors, dependents and path policy
opendrs hotspotsRank files by risk at rest
opendrs calibrate / opendrs backtestFit the model to your history; compare default and calibrated models
opendrs feedbackRecord an incident in .opendrs/incidents.jsonl
opendrs initWrite a commented .opendrs.yml and a comment-only workflow
opendrs mcpRun the MCP server

Flags, exit codes, MCP tools and the agent skill: CLI, MCP server, Claude Code plugin and agent skill.

Documentation

PageRead it when you want to
Auto-approve low-risk PRsset up a risk-based approval gate step by step
GitHub Action referencelook up inputs, outputs, permissions, branch rules and the scheduled sweep
Policy and safety railsknow exactly why a PR was or was not approved
Configuration referencechange .opendrs.yml
Risk signalssee what opendrs measures and how
What is the blast radius of a PR?understand blast radius
LLM judgeturn on the optional LLM judge or the requireSafe gate
Feeding the review brief to an LLM reviewerget opendrs's findings in front of Claude Code, claude-code-action, Bugbot, CodeRabbit, Copilot or Codex
The captain briefknow what the final human reviewer sees: priority, reviewer, and the architecture and history facts
Verification passturn on an LLM re-check of the brief's facts before a human reads them

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