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PopNx MCPMCP servers for people who use AI · explained in plain words

Orcareplay

by Continuum-AI-Corpversion 0.5.0i★ 480iRuns on your computer
Registry nameio.github.Continuum-AI-Corp/orcareplay

Install

Overview

Description from the maker (original English)

Read, replay and fork recorded coding-agent runs.

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: 2 of 2 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

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": {
    "orcareplay": {
      "command": "npx",
      "args": [
        "-y",
        "orcareplay"
      ]
    }
  }
}

Run this in your terminal

claude mcp add orcareplay -- npx -y orcareplay

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "orcareplay": {
      "command": "npx",
      "args": [
        "-y",
        "orcareplay"
      ]
    }
  }
}

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": {
    "orcareplay": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "orcareplay"
      ]
    }
  }
}

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.

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Run on your computer in

ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast

All apps, with pros and cons →

Related guides

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 11, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account Continuum-AI-Corp, which owns the repo

  • ✓ Pass

    Still maintained

    Code last changed 41 days ago

  • … 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

User reviews

No reviews yet

Sign in with Google to write a review

Compared with others in Developer toolsi

  • More stars than 99% of the other 5742 servers in this category
  • Published to the registry more recently than 45% of the other 5742 servers in this category
  • Basic checks equal to or better than 100% of the other 5742 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 Orcareplay safe?

It passed 2 of 2 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 41 days ago, and the latest version (0.5.0) reached the registry 42 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: REA, Netdata, Front-End Checklist

Alternatives

Servers in the same category from other makers, sorted by stars.

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

REA

Reverse engineer anything from your terminal or agent with one CLI and MCP server.

by morlutoWidely used↓ 388.7K/wk★ 448.2KUpdated 17 days ago

Runs on your computer

Netdata

Real-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.

by netdataWidely used★ 81.3KUpdated 6 days ago

Runs in the cloud

Front-End Checklist

Review frontend code and live pages against 386 quality-gated web development rules.

by thedaviddiasWidely used★ 74.7KUpdated 37 days ago

Runs in the cloud

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.

OrcaReplay

English · 简体中文 · 日本語 · 한국어 · Deutsch · Français · Español · العربية

Your agent broke something at 2am. Replay it at 9am — exactly, offline, as many times as you like.

Record any coding agent. Reproduce the run byte-for-byte with no model called. Fork it from any step onto a different model and see who gets it right.

Built by the team behind [OrcaRouter](https://www.orcarouter.ai) — one API key and one endpoint for Claude, GPT, Gemini, Grok, DeepSeek, Qwen and the rest. It is what orca setup points at by default, and what makes orca compare a single command instead of four provider accounts.

Find us: OrcaRouter All model APIs

Github Repos: OrcaCode Review · OrcaRouter Lite

Connect: X · Discord · Hugging Face · Ollama

Real output from one session — a Claude Code run recorded, replayed against the recording, then forked at checkpoint 4 onto two models and graded by npx tsc --noEmit. Nothing here is mocked up.

Try it in three commands

One npm package named orcareplay, which puts one command named orca on your PATH. There is no separate tool called "orca", and nothing is installed into your agent.

npm i -g orcareplay             # the package is orcareplay; the command it installs is orca

orca record claude # your agent, unmodified, doing whatever it does orca replay last # the same run again — no network, no tokens, no charge orca replay last --from 4 --model claude-haiku-4-5 --ui

The third line is the one people stay for: same files, same conversation prefix, different model from step 4 onward. The model is the only variable, which is what makes the answer mean anything.

The three commands at the top need an agent installed, a key, a network and real tokens. If you have none of those yet, one command brings its own:

orca quickstart

It writes a small project with a genuine bug in it and a recording of an agent fixing that bug, then replays the recording against the project with no model called: two failing tests before, four passing after, three turns served from the trace and nothing spent. --full prints the whole timeline and the replay as it happened.

Read your agent's own system prompt

A proxy that sees the whole loop also sees the prompt the harness assembled before it sent anything. One command captures it, scrubs the machine out of it, and files it by model:

node capture/capture.mjs claude --model claude-opus-5

Interactive prompts and -p prompts are not the same prompt, and neither is the same across models. See capture/README.md for the measured differences, the pitfalls, and the sanitising rules.

Why this exists

Agent debugging today is archaeology. You scroll a terminal, you re-run and get a different failure, you add print statements to someone else's harness. The tools that exist are observability tools: they tell you a run cost $4.12 and used 61k tokens, which is not the question you have. The question you have is *why did it delete my migration file.*

OrcaReplay answers that by giving you the run back.

Observability toolsOrcaReplay
Tells you what a run cost✅✅
Tells you which tool call deleted the filesometimes✅
Runs the agent again and gets the same answer❌✅ from the recording, byte-for-byte
Lets you change the model and re-run from step 4❌✅
Needs you to modify your agentusually an SDK wrapper❌ two env vars
Works after you close the terminal❌✅ it is a file
Sees past the model API — shell exit codes, file writes❌✅ every turn
Records an agent with no API endpoint to redirect❌✅ opt-in --tls-intercept

The last two rows are the ones an SDK wrapper structurally cannot reach. Capture happens *below* the agent — at the process and socket boundary — so it does not matter whether the agent is yours, whether you can edit it, or whether it even holds an API key: a Codex CLI signed in with a ChatGPT subscription talks to its own backend over TLS, with a credential that only that backend accepts, so it cannot be pointed elsewhere and stay the same session — and orca can still record it. See when the harness will not be redirected.

How it works

Model APIs are stateless, so on every turn an agent resends the entire conversation — including the previous turn's tool results. A proxy in front of the model therefore sees the whole loop: each request, each streamed response, every tool call the model emitted, and every tool result the harness produced. That one property is what the tool is built on, and it is why OrcaReplay does not patch your agent — it stands up a local proxy, sets two environment variables, and gets out of the way.

What "egress blocked" means, exactly. On replay the proxy refuses to forward anything it cannot serve from the trace, so no model is called and no tokens are spent — that run prints egress=blocked. --loose lifts it deliberately: an unmatched request is then answered by the provider and recorded as a major divergence, and the same line reads egress=live-on-unmatched. A replay still *executes the recorded tool calls for real*, and a tool that opens its own socket — a shell command running curl, an MCP server fetching something — is outside the guarantee either way. By default it never reaches the proxy and goes to the network as usual. Under --tls-intercept it does reach the proxy, because that sets HTTPS_PROXY for the whole child: a host off the intercept list is tunnelled through untouched, though its hostname, port and byte counts still land in the trace, and a host on the list is refused there like any other unmatched call. Replay is not a sandbox; if you need one, run it inside one.

Three more layers catch what the protocol cannot see: an exit code, a real duration, which stream a byte came out of, a file written without telling anyone. A fifth exists for the agents that read no base-URL variable at all — see which agents. A sixth reads the agent's own account of its structure, for a harness that has one: which sub-agent ran, which handed off to which, whether a guardrail tripped, which graph node produced what and which of them called no model at all — none of which reaches the wire.

%%{init: {'theme':'neutral'}}%%

flowchart LR A[" your agent unmodified "]

subgraph orca["orca · six capture layers"] direction TB P[" proxy base-URL env var"] SH[" PATH shim exit code · timing · streams"] MC[" JSON-RPC tee MCP config rewrite"] FS[" shadow git index workspace per turn"] FH[" fetch hook for a hardcoded origin"] end

A --> P & SH & MC & FS & FH P -->|"forwarded, auth intact"| U[" the model API or OrcaRouter · any gateway "] orca ==> T[(" one trace .orca/runs/run_a1b2c3")]

They all land in the same timeline, ordered by when they actually happened rather than when orca got around to reading them.

Exact, fork and compare are one thing

They are not three subsystems. They are the same proxy with a cursor — the position in the recorded stream where it stops answering from disk and starts answering from the network.

%%{init: {'theme':'neutral'}}%%

flowchart LR subgraph disk["from disk · byte-for-byte · network blocked"] direction LR T1["turn 1"] --> T2["turn 2"] --> T3["turn 3"] --> T4["turn 4"] end T4 ==> CUR{{" cursor "}} CUR ==> T5 subgraph net["from the network · any model you name"] direction LR T5["turn 5"] --> T6["turn 6"] --> T7["…"] end

commandwhere the cursor sitswhat you get
orca replay lastat the endthe whole run again, network blocked — no tokens, no charge, no variance
orca replay last --from 4 --model Xat checkpoint 4turns up to 4 identical, then a different model takes over
orca compare last --from 4 --models a,bat checkpoint 4, several timesone table, one variable — the model

A checkpoint is not recorded; it is *derived* — any point where the conversation prefix is complete and the workspace was snapshotted. Every fork therefore starts from a state that provably existed.

What a bug hunt actually looks like

Your agent was supposed to fix a failing auth test. It exited 0 and the test still fails. Start with what it actually did:

$ orca show last

run_6473f858b59e generic-openai@0.1.0 14 events exit 0

SEQ KIND WHAT DETAIL 0 RUN run started generic-openai 1 SNAP tree 919d32ba037537b43814c83779963b2cc3023db7 0 changed 2 MODEL claude-opus-5 1 messages 3 MODEL claude-opus-5 stop: tool_use · 100 in · 20 out 4 TOOL edit_file {"path":"auth.ts",…} 5 SNAP tree c6af62b75c0c8b8938bd6087328b5148f3dcd534 1 changed 6 FILE auth.ts modified +1 −3 7 TOOL edit_file ok 8 MODEL claude-opus-5 3 messages 9 MODEL claude-opus-5 stop: end_turn · 101 in · 5 out 10 SNAP tree c6af62b75c0c8b8938bd6087328b5148f3dcd534 0 changed 11 SHELL ["sh","-c","node --check nonexistent-file.ts"] /tmp/hunt 12 SHELL shell result exit 1 · 43ms 13 RUN run ended exit 0

info usage input=201 output=25 cost=$0.004890

Three facts the model's own transcript could not have told you, and the run's exit code hid: the file really changed (seq 6, +1 −3), the check the agent ran failed (seq 12, exit 1), and it finished anyway. The run exited 0 because the *agent* exited 0.

That last fact is the one worth a command of its own. orca show gives you the order things happened in; orca graph gives you what produced what:

$ orca graph last

FROM TO KIND WHY 3 model.response 4 tool.call recorded tool_use block in the response 4 tool.call 6 fs.change inferred changed path appears in tool input, same or previous turn 4 tool.call 7 tool.result recorded tool result answers its call 7 tool.resu

Only the beginning is shown; read the rest in the repo