Supabase
Supabase’s official server lets AI create tables, query data and review project settings in your Supabase database.
Computer or cloud✓ supabase.com
Penguin Statistics
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.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi2 of 3 passedChecked from public information; code not scanned yet
- UsageiVery new★ –
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 in the cloud
- Transport: Streamable HTTP
- Category: Databases & datasets
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 2 of 3 passed
- Runs on the maker’s server: what you ask it goes through their server.
- Can do 6 things (tools) get_dataset_info, get_item_drop_rates, get_stage_drops, list_events …
- The registry lists no key to fill in.
You need
- Runs in the cloudNothing to install; it connects over the internet
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:
- Open Settings → Connectors
- Press Add custom connector
- Paste the address below and press Add
- If it asks you to sign in, sign in with the maker
https://mcp.penguin-stats.io/mcpRun this in your terminal
claude mcp add --transport http penguin-stats https://mcp.penguin-stats.io/mcpOr 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.
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:
- Open ChatGPT on the web → Settings → Connectors (Apps)
- Press Create or Add (some plans need Developer mode under Advanced first)
- Paste the address below, give it a name and save
- If it asks you to sign in, sign in with the maker
https://mcp.penguin-stats.io/mcpMenu 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
Tool names and descriptions exactly as the server reported them on Nov 7, 2026. The maker wrote them, not us; we show them as plain text.
get_dataset_infoDescribes the Penguin Statistics drop data: what its numbers mean (times, quantity, drop rate, sanity per item, window), the game servers with their time zones and how fresh the data of each is, the caveats, the license and how to credit it. Call it when unsure how to read or cit
get_item_drop_ratesWhere to farm an item: ranks every stage of a game server that drops it, with sanity per item, drop rate (items per run), runs (times) and quantity. Use it for any question about where to get an item, its best or most efficient stage, or which of several stages to run for it: one
get_stage_dropsWhat one stage drops on a game server: each item with drop rate (items per run), sanity per item, runs (times) and quantity, highest rate first, then the items it no longer drops; with the sanity cost of the stage and whether it is open now. For questions about a given stage. To
list_eventsLists the events (limited-time activities) of a game server: status now (open, upcoming or closed), open and close times, whether a rerun. By default the open and upcoming events with their stage IDs (for get_stage_drops), or the latest 3 if there are none; open_only=false lists
search_itemsFinds items (materials, battle records, chips, furniture, event items) by name in any language (zh, en, ja, ko), by alias or nickname (土块, 狗粮), by pinyin (guyuanyan) or by item ID, and returns their item IDs (get_item_drop_rates takes a name too; search first when unsure which it
search_stagesFinds stages by code (1-7, S2-12, YW-4; full-width characters work too), by stage ID (main_01-07), or by the name or ID of their zone (第十章, Under Tides), and returns their stage IDs (for get_stage_drops; to find the stages that drop an item, use get_item_drop_rates) with their zo
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 9, 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
- ✓ Pass
Endpoint responds
Responded in 503 ms (HTTP 200)
- – Not applicable
Sign-in with OAuth
No sign-in needed
- ✓ Pass
No hidden instructions in tool descriptions
Read 6 tool descriptions and found no hidden instructions to the AI
Compared with others in Databases & datasetsi
- More stars than 0% of the other 1153 servers in this category
- Published to the registry more recently than 70% of the other 1153 servers in this category
- Basic checks equal to or better than 40% of the other 1153 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 2 of 3 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 17 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: Supabase, MongoDB MCP Server, Elasticsearch MCP Server
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
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/mcpThe 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, likeREPORT.SINGLEPenguin-Original-Sequence: its sequence inpenguin-reportsPenguin-Deliveries: its deliveries when it movedPenguin-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 headersnats 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 127.0.0.1:5434regenerate 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 rungo 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!