Supabase
MCP server for interacting with the Supabase platform
作者提供的说明(英文原文)
Declarative synthetic data: multi-table data that holds the facts you declare, checked by DuckDB.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"misata": {
"command": "uvx",
"args": [
"misata"
]
}
}
}在终端中运行这条命令
claude mcp add misata -- uvx misata粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"misata": {
"command": "uvx",
"args": [
"misata"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"misata": {
"type": "stdio",
"command": "uvx",
"args": [
"misata"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
注册名称已通过 GitHub 账户 rasinmuhammed 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.9.8)于12天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
同一分类中有:Supabase, MongoDB MCP Server, Elasticsearch MCP Server
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP server for interacting with the Supabase platform
MongoDB Model Context Protocol Server
MCP server for interacting with Elasticsearch
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Misata is a declarative synthetic data generator for Python. You write down the facts a dataset has to satisfy, from a monthly revenue curve to a balanced ledger to a foreign key that must never dangle, and the engine builds multi-table data that satisfies all of them at once. Then DuckDB checks the files on disk and writes a certificate anyone can re-run.
Docs · Use cases · The language · Limitations · Studio · Changelog
pip install "misata[duckdb]"
# misata.yaml
seed: 7 tables: customers: rows: 2000 columns: customer_id: {type: int, unique: true, min: 1, max: 2000} name: {type: text, text_type: name} signed_up: {type: date, start: "2024-01-01", end: "2024-12-31"} orders: rows: 40000 columns: order_id: {type: int, unique: true, min: 1, max: 1000000} customer_id: {type: foreign_key} channel: {type: categorical, choices: [web, app, store]} amount: {type: float, min: 5, max: 900} ordered_at: {type: date, start: "2025-01-01", end: "2025-12-31"} relationships:
outcome_curves:
group_shares:
misata generate -c misata.yaml -o data --attest
python data/verify.py
Attestation: data
result: PASSED files pinned: 2 invariants held: 69 of 69
ok 2 file(s) match their attested sha256 ok 69 of 69 invariant(s) hold
The 69 are derived from the YAML before a row exists: twelve monthly revenue totals (180,000.00 in January, 240,000.00 in June, 310,000.00 in December, straight lines between), the channel split inside every one of those months, the planned row count per month, zero orphaned customer_ids and unique keys. verify.py needs only the standard library and duckdb, and it shares no code with the generator.
The YAML above, generated and summed by month and channel. Each bar is a month of generated revenue split by channel; the cap is the total the YAML declares. Drawn by readme_charts.py from a fresh run of this README's own YAML.
Change one cent in one row and it says so:
FAIL orders.csv: sha256 301165f9dc4e does not match the attested 670f89195826
FAIL orders.amount.month:1.outcome_curve_period: expected 180000.00, observed 180000.01 FAIL orders.amount.outcome_curve_total: expected 2945000.00, observed 2945000.01 FAIL orders.amount.web.2025-01.group_share_period: expected 90000.00, observed 90000.01 FAIL orders.amount.web.group_share_total: expected 1472500.00, observed 1472500.01 FAIL 65 of 69 invariant(s) hold
Every test fixture has an oracle problem. To check a dbt model, a Spark job, a dashboard tile or a data agent you need input data and an answer you know independently of the code under test. Copying production gives you data without an answer. Hand-written fixtures give you an answer that rots. Random generators give you data whose answer you have to compute with the code you are testing.
Misata turns it around. The specification is the ground truth and the rows are derived from it. "January revenue is 180,000" is true before a single row exists, so your pipeline either returns 180,000.00 or it has a bug.
flowchart LR
A["Declare curves, shares, ledgers, keys, lifecycles"] --> B{"Can they all hold?"} B -- "no" --> R["Refused, with the arithmetic and a fix"] B -- "yes" --> C["Build rows closed form where it can, by construction elsewhere"] C --> D["Write CSV, Parquet, JSONL, DuckDB, Postgres"] D --> E["Attest DuckDB re-runs every derived invariant"] E --> F["Certificate in-toto Statement plus verify.py"]
Three properties make this different from a generator with options:
Misata's aim is that nobody writes a Python script to make a dataset, of any kind. You say what has to be true; the engine works out the rows, refuses what cannot hold, and proves what it built.
It is not there yet, and it is getting there brick by brick. Each release adds things you can declare instead of programming, and a brick is laid only when it passes three tests: it can be declared in a few lines of YAML, it is refused before generation when it contradicts another declaration, and it is re-checked on the rows afterwards by code that did not write them.
Every brick is something you declare instead of code. Drawn from misata/registry.py , the list a contract test holds the code to, so the wall cannot claim a brick the engine does not have.
One fact, two ways. What a script has to do to make a fact true, and what you write instead:
| The fact | In a generator script | In Misata |
|---|---|---|
| January revenue is 180,000.00 | draw amounts, sum them, rescale, round, chase the last cent | {month: 1, target_value: 180000} |
| web is half of every month's revenue | rescale inside each month and channel, then repair the monthly totals you just broke | shares: {web: 0.5, app: 0.3, store: 0.2} |
| every order has a real customer | sample ids from the parent table and keep both in step | customers.customer_id -> orders.customer_id |
| some churned customers were active first | branch on status, write timestamps in order, null the rest, count by hand | transitions: [[trialing, active], [active, churned]] |
| 1% of transfers form laundering patterns, in a set mix | build each ring and fan by hand, then prove the background has no cycle of its own | rate: 0.01 and shares: {cycle: 0.4, fan_in: 0.25, ...} |
| the trial balance is zero | post both legs of every entry and reconcile at the end | a balanced_ledger constraint on the journal lines |
What still takes code, and where each one stands:
| Still scripted by hand | Where it stands |
|---|---|
| The expected output of logic Misata cannot invert: window functions, custom SQL, business rules | misata solve drives your real dbt, SQL or Python pipeline to a declared number within a stated tolerance; past that, you write the expected value |
| Correlations you can only describe by example | misata.mimic fits them from real rows, and its output is not anonymous |
| Free text with a voice of its own | text is generated from grammars and agrees with its row; prose that reads as written is not declarable yet |
| Version histories (SCD2) and inventory trajectories | generated to balance, not yet declared step by step |
| Documents, images and audio | not started |
LIMITATIONS.md keeps the full list. When a line leaves this table, it is because the brick that replaces it has passed all three tests.
The Gauntlet: 13 tables and 136 SQL assertions run by DuckDB. The Faker script ties wherever a fact is about one column at a time and loses wherever columns, rows or tables have to agree. Source , rerun with python -m benchmarks.gauntlet .
<img src="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/detection-light.svg" width="680" alt="Detection AUC, lower is better, 0.5 means a classifier cannot tell generated rows from real ones. Olist orders: Misata from a declared schema 0.63, Misata from a sentence 0.73, Faker script 0.78, SDV fitted to the real data 0.71, Misata mimic 0.54. NYC taxi trips: 0.
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