原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Misata: synthetic data library

作者:rasinmuhammed版本 0.9.8i★ –i在你的电脑上运行
注册名称io.github.rasinmuhammed/misata

安装

概览

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.

PopNx MCP 的三大支柱

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。

它能帮你做什么

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。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 数据库与数据集

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

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

在 Cursor 中打开

或者自己复制设置

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。

在 VS Code 中打开

或者自己复制设置

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。

它能做什么

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。

基础检查i

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

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 rasinmuhammed 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    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“数据库与数据集”中的其他服务器比较i

  • 星标数多于此分类中其他 1144 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 1144 个服务器的 75% 更新
  • 基础检查结果等于或优于此分类中其他 1144 个服务器的 100%

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。

常见问题

Misata: synthetic data library 安全吗?

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。

它需要哪些密钥或代码?

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。

它在我的电脑上运行还是在云端?

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)。

它还在维护吗?

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。

哪些应用可以使用它?

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)。

有什么可以替代它?

同一分类中有:Supabase, MongoDB MCP Server, Elasticsearch MCP Server

替代选择

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

Supabase

作者:supabase

MCP server for interacting with the Supabase platform

电脑或云端★ 2,993Apache-2.0昨天更新
安装

MongoDB MCP Server

作者:mongodb-js

MongoDB Model Context Protocol Server

在你的电脑上运行★ 1,157Apache-2.03天前更新
安装

MCP server for interacting with Elasticsearch

在你的电脑上运行★ 315Apache-2.014个月前更新
安装

作者的 README

打开 README

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

Declare what must be true. Get data that makes it true. Prove it.

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


Sixty seconds

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:

  • customers.customer_id -> orders.customer_id

outcome_curves:

  • table: orders column: amount time_column: ordered_at start_date: "2025-01-01" curve_points:
  • {month: 1, target_value: 180000}
  • {month: 6, target_value: 240000}
  • {month: 12, target_value: 310000}

group_shares:

  • table: orders measure: amount group_column: channel shares: {web: 0.5, app: 0.3, store: 0.2}
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


Why declare instead of generate

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:

  • A declaration is honoured or refused, never quietly replaced. Declarations that cannot all hold stop generation with an error that names each one, shows the arithmetic and suggests the change.
  • The certificate is checked by code that did not write the data. A bug in Misata's arithmetic shows up as a failed invariant, not a self-consistent wrong answer.
  • The same spec, seed and version give the same bytes on Linux, macOS and Windows, so a fixture can live in git with its fingerprint.

Where this is going

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 factIn a generator scriptIn Misata
January revenue is 180,000.00draw amounts, sum them, rescale, round, chase the last cent{month: 1, target_value: 180000}
web is half of every month's revenuerescale inside each month and channel, then repair the monthly totals you just brokeshares: {web: 0.5, app: 0.3, store: 0.2}
every order has a real customersample ids from the parent table and keep both in stepcustomers.customer_id -> orders.customer_id
some churned customers were active firstbranch on status, write timestamps in order, null the rest, count by handtransitions: [[trialing, active], [active, churned]]
1% of transfers form laundering patterns, in a set mixbuild each ring and fan by hand, then prove the background has no cycle of its ownrate: 0.01 and shares: {cycle: 0.4, fan_in: 0.25, ...}
the trial balance is zeropost both legs of every entry and reconcile at the enda balanced_ledger constraint on the journal lines

What still takes code, and where each one stands:

Still scripted by handWhere it stands
The expected output of logic Misata cannot invert: window functions, custom SQL, business rulesmisata 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 examplemisata.mimic fits them from real rows, and its output is not anonymous
Free text with a voice of its owntext is generated from grammars and agrees with its row; prose that reads as written is not declarable yet
Version histories (SCD2) and inventory trajectoriesgenerated to balance, not yet declared step by step
Documents, images and audionot 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 evidence

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.

只显示了开头部分;其余内容请在仓库中阅读

问 PopNx