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Penguin Statistics

by penguin-statisticsversion 1.0.0i★ –iRuns in the cloud
Registry nameio.penguin-stats/penguin-stats

Install

Overview

Description from the maker (original English)

Crowdsourced Arknights drop rates by stage and item, for the CN, US, JP and KR game servers

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.

Install

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

Before you install

  • Basic checks: 0 of 1 passed
  • Runs on the maker’s server: what you ask it goes through their server.
  • 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 …”.

Works in Claude on the web, desktop and mobile:

  1. Open Settings → Connectors
  2. Press Add custom connector
  3. Paste the address below and press Add
  4. If it asks you to sign in, sign in with the maker
SettingsConnectors1+ Add2https://…3
https://mcp.penguin-stats.io/mcp

Run this in your terminal

claude mcp add --transport http penguin-stats https://mcp.penguin-stats.io/mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "penguin-stats": {
      "url": "https://mcp.penguin-stats.io/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": {
    "penguin-stats": {
      "type": "http",
      "url": "https://mcp.penguin-stats.io/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.

ChatGPT connects only to servers on the internet:

  1. Open ChatGPT on the web → Settings → Connectors (Apps)
  2. Press Create or Add (some plans need Developer mode under Advanced first)
  3. Paste the address below, give it a name and save
  4. If it asks you to sign in, sign in with the maker
SettingsConnectors1+ Create2https://…3
https://mcp.penguin-stats.io/mcp

Menu names can differ slightly by plan and over time.

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

We have not read this server’s tool list yet; we check endpoints in rotation.

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

  • ! Caution

    Maker matches the repo

    The publisher (penguin-stats.io) is not the repo owner (penguin-statistics); someone else may have listed it

  • … 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 checked yet

    Endpoint responds

    Not checked yet; we check endpoints in rotation

  • … Not checked yet

    Sign-in with OAuth

    Not checked yet; we check endpoints in rotation

  • … Not checked yet

    No hidden instructions in tool descriptions

    Not checked yet; we read the tool list when we check the endpoint

Compared with others in Otheri

  • More stars than 0% of the other 32461 servers in this category
  • Published to the registry more recently than 72% of the other 32461 servers in this category
  • Basic checks equal to or better than 33% of the other 32461 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 Penguin Statistics safe?

It passed 0 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 in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 14 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, dotnet-mcp

Alternatives

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

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

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago
Install

Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday
Install

dotnet-mcp

by Skymly

MCP server for .NET symbols, XAML, source generators, and restricted workspace edit

Runs on your computerUpdated today
Install

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.

Penguin Statistics - V3 Backend

The re-designed Penguin Statistics v3 Backend, built with Go.

Using the data

The drop data is public:

  • REST API at https://penguin-stats.io/PenguinStats/api/v2/: see the developer docs and the OpenAPI (Swagger 2.0) spec.
  • MCP server for AI assistants (Claude, ChatGPT, Cursor, VS Code, Cherry Studio and others) at https://mcp.penguin-stats.io/mcp: Streamable HTTP, no authentication, read-only. In Claude Code:
  claude mcp add --transport http --scope user penguin-stats https://mcp.penguin-stats.io/mcp

The steps for every other app, in English and Chinese, are at https://mcp.penguin-stats.io/.

  • Plugin and skill for coding agents, the MCP server with a skill on reading, citing and querying the numbers: https://github.com/penguin-statistics/agent-skills

The drop data is licensed CC BY-NC 4.0: non-commercial use, with credit. Credit it as Data: Penguin Statistics (penguin-stats.io), CC BY-NC 4.0. The code of this repository is MIT.

Architecture

This Go project mainly uses:

This project mainly follows a RESTful API design.

Operations

Report tasks in the dead-letter stream

The report worker moves a report task out of the penguin-reports stream into penguin-reports-dead (subjects REPORT_DEAD.*) when it can never be processed (it is not a task, or a stage of it does not exist) or when it failed at each of its 30 deliveries, which take about 97 minutes of retry delays. Each move is logged at error level (reportwkr.malformed, reportwkr.parked or reportwkr.dead). The copy keeps the payload as published, with these headers:

  • Penguin-Original-Subject: the subject it was published under, like REPORT.SINGLE
  • Penguin-Original-Sequence: its sequence in penguin-reports
  • Penguin-Deliveries: its deliveries when it moved
  • Penguin-Last-Error: the error that moved it

A task too close to the maximum payload to fit with these headers moves without them, and its subject under REPORT_DEAD.* names its original subject; its move is logged as reportwkr.dead.bare, with the sequence and the error.

Nothing in the stream expires. With the nats CLI, once the cause is fixed:

# list the tasks, with their headers

nats stream view penguin-reports-dead

show one, by its sequence in penguin-reports-dead

nats stream get penguin-reports-dead 42

replay it under its original subject: the worker processes it as a new task

nats stream get penguin-reports-dead 42 --json | jq -r .data | base64 -d \ | nats pub REPORT.SINGLE --force-stdin

remove it once replayed

nats stream rmm penguin-reports-dead 42 -f

A task that moved after failing at each delivery may have been stored by one of them whose commit outcome was unknown (for example the connection dropped during the commit); check drop_reports for its account and time before replaying it.

Benchmarks

Report consumer CPU

internal/workers/reportwkr/consumerbench_test.go (build tag consumerbench) drives the report consumer through its real code path: each task is published to a JetStream stream of its own, fetched and handed to Worker.ingestPreprocess (BenchmarkConsumer/ingest), which stores it in PostgreSQL and sets its recall key in Redis, with logging and tracing configured as in production. BenchmarkConsumer/process runs Worker.process alone. Besides wall time and allocations it reports, per task, the CPU time of the process (cpu-ns/op, user + system from getrusage, which a shared machine disturbs far less than the wall time) and the time spent waiting on PostgreSQL (pg-wait-ns/op, pg-wait-%) and Redis.

The tasks (internal/workers/reportwkr/testdata/consumerbench/tasks.jsonl.gz) are encoded as the report handler publishes them, drawn from the stages, servers, times, sources, versions and drop patterns of recent production reports, with synthetic accounts, IPs and task ids.

It needs a scratch database, Redis and NATS with JetStream. The scratch database holds the schema and the reference tables of a restore of production (penguinprod, only read) and synthetic accounts; the benchmark truncates its drop_reports and drop_report_extras:

scripts/consumerbench/setup-db.sh   # creates penguin_cpubench and the role cpubench on 127.0.0.1:5434

regenerate the tasks, if needed: CONSUMERBENCH_TASKS, CONSUMERBENCH_BATCH_PERMILLE, CONSUMERBENCH_SEED

CONSUMERBENCH_GENERATE=1 go test -tags consumerbench -run TestConsumerBenchGenerate ./internal/workers/reportwkr/

The defaults point at penguin_cpubench on 127.0.0.1:5434, Redis DB 14 on 127.0.0.1:16379 and NATS on 127.0.0.1:4222 (CONSUMERBENCH_POSTGRES_DSN, CONSUMERBENCH_REDIS_URL, CONSUMERBENCH_NATS_URL). Then:

# one run

go test -tags consumerbench -run '^$' -bench BenchmarkConsumer -benchtime 2000x ./internal/workers/reportwkr/

interleaved runs of variants, compared with benchstat: logging to a counting io.Discard (prod), to /dev/null

(devnull), disabled (logoff); tracing as in production at 10% (prod), off (traceoff), at 100% (trace1); both off (off)

COUNT=10 scripts/consumerbench/run.sh bench prod logoff traceoff off devnull trace1

CPU and allocation profiles of the ingestion, the top functions and the share of each part of the consumer

scripts/consumerbench/run.sh profile prod

Outputs go to $OUT (default a new temporary directory). Each run logs the load average and the queries other sessions had active on the cluster to $OUT/runs.log. The PostgreSQL connection runs over loopback, where a write system call also does the receiving side's TCP work, so the system calls weigh more than against a remote server.

Maintainers

This project has mainly being maintained by the following contributors (in alphabetical order):

The full list of active contributors of the _Penguin Statistics_ project can be found at the Team Members page of the website.

Contributors

This project is made possible by the following contributors. Contributions are always welcome!

License

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