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。
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IterableData

作者:datenoio版本 1.1.0i★ –i在你的电脑上运行
注册名称io.github.datenoio/iterabledata

安装

概览

作者提供的说明(英文原文)

Streaming read, convert, and inspect 100+ formats. Install iterabledata[mcp] and run iterable-mcp.

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: 其他

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": {
    "iterabledata": {
      "command": "uvx",
      "args": [
        "iterabledata"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add iterabledata -- uvx iterabledata

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": {
    "iterabledata": {
      "command": "uvx",
      "args": [
        "iterabledata"
      ]
    }
  }
}

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": {
    "iterabledata": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "iterabledata"
      ]
    }
  }
}

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7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 datenoio 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“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32589 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32589 个服务器的 80% 更新
  • 基础检查结果等于或优于此分类中其他 32589 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

IterableData 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(1.1.0)于8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:Spec Driven Development, Remote Desktop Commander, Detailing Manager

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

Spec Driven Development

作者:formulahendry

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

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

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

在云端运行★ 211昨天更新
安装

Detailing Manager

作者:RadoslavMoshev

Connect your auto detailing studio's tasks, cars, clients and revenue to Claude or ChatGPT.

在你的电脑上运行今天更新
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。

Iterable Data

Iterable Data is a Python library for reading and writing data files row by row in a consistent, iterator-based interface. It provides a unified API for working with various data formats (CSV, JSON, Parquet, XML, etc.) similar to csv.DictReader but supporting many more formats.

This library simplifies data processing and conversion between formats while preserving complex nested data structures (unlike pandas DataFrames which require flattening).

Features

  • Unified API: Single interface for reading/writing multiple data formats
  • Automatic Format Detection: Detects file type and compression from filename or content (magic numbers and heuristics)
  • Format Capability Reporting: Programmatically query format capabilities (read/write/bulk/totals/streaming/tables)
  • Support for Compression: Works seamlessly with compressed files
  • Preserves Nested Data: Handles complex nested structures as Python dictionaries
  • DuckDB Integration: Optional DuckDB engine for high-performance queries with pushdown optimizations
  • Pipeline Processing: Built-in pipeline support for data transformation
  • Encoding Detection: Automatic encoding and delimiter detection for text files
  • Bulk Operations: Efficient batch reading and writing
  • Native Batch Conversion: Opt-in columnar-to-columnar transfers with projection, row-range, and batch-size selection
  • Bounded Columnar I/O: Shared row/bulk cursors and configurable Parquet row groups keep large reads and writes bounded
  • Codec Performance Profiles: Choose fast, balanced, or max compression settings with effective-setting diagnostics
  • Table Listing: Discover available tables, sheets, and datasets in multi-table formats
  • Context Manager Support: Use with statements for automatic resource cleanup
  • DataFrame Bridges: Convert iterable data to Pandas, Polars, and Dask DataFrames with one-liner methods
  • Cloud Storage Support: Direct access to S3, GCS, and Azure Blob Storage via URI schemes
  • Database Engine Support: Read-only access to SQL and NoSQL databases (PostgreSQL, ClickHouse, MySQL, MongoDB, Elasticsearch, etc.) as iterable data sources
  • Atomic Writes: Production-safe file writing with temporary files and atomic renames
  • Bulk File Conversion: Convert multiple files at once using glob patterns or directories
  • Progress Tracking and Metrics: Built-in progress bars, callbacks, and structured metrics objects
  • Error Handling Controls: Configurable error policies and structured error logging; malformed input raises typed errors by default instead of reading as empty datasets
  • Security Hardening: XXE-safe XML parsing, AST-whitelisted filter expressions, and explicit pickle trust acknowledgement
  • Performance Regression Gate: CI-enforced baselines for representative read/convert workloads
  • Container Formats: Stream records from TAR archives without extracting members to disk
  • Type Hints and Type Safety: Complete type annotations with typed helper functions for dataclasses and Pydantic models
  • Lakehouse Tables: Read and write Delta Lake, Iceberg, and DuckLake; read Hudi; experimental Apache Paimon tables plus Row/Mosaic file formats

Supported File Types

Core Formats
  • JSON - Standard JSON files
  • JSONL/NDJSON - JSON Lines format (one JSON object per line)
  • JSON-LD - JSON for Linking Data (RDF format)
  • CSV/TSV - Comma and tab-separated values
  • Annotated CSV - CSV with type annotations and metadata
  • CSVW - CSV on the Web (with metadata)
  • PSV/SSV - Pipe and semicolon-separated values
  • LTSV - Labeled Tab-Separated Values
  • FWF - Fixed Width Format
  • XML - XML files with configurable tag parsing
  • ZIP XML - XML files within ZIP archives
  • HTML - HTML files with table extraction
Binary Formats
  • BSON - Binary JSON format
  • MessagePack - Efficient binary serialization
  • CBOR - Concise Binary Object Representation
  • UBJSON - Universal Binary JSON
  • SMILE - Binary JSON variant
  • Bencode - BitTorrent encoding format
  • Avro - Apache Avro binary format (read & write)
  • Pickle - Python pickle format (untrusted input is unsafe; pass trust=True to acknowledge)
Columnar & Analytics Formats
  • Parquet - Apache Parquet columnar format
  • ORC - Optimized Row Columnar format
  • Arrow/Feather - Apache Arrow columnar format
  • GeoParquet - GeoParquet metadata-aware Parquet profile with geometry/CRS preservation
  • Lance - Modern columnar format optimized for ML and vector search
  • Vortex - Modern columnar format with fast random access
  • Paimon Row - Apache Paimon row format for O(1) row-number access
  • Paimon Mosaic - Apache Paimon columnar-bucket format for wide tables
  • Paimon - Apache Paimon warehouse/catalog tables
  • Delta Lake - Delta Lake format (read & write)
  • Iceberg - Apache Iceberg format (read & write)
  • DuckLake - DuckLake lakehouse tables (read & write)
  • Hudi - Apache Hudi format (read; writes deferred)
Database Formats
  • SQLite - SQLite database files
  • DBF - dBase/FoxPro database files
  • MySQL Dump - MySQL dump files
  • PostgreSQL Copy - PostgreSQL COPY format
  • DuckDB - DuckDB database files
Statistical Formats
  • SAS - SAS data files
  • Stata - Stata data files
  • SPSS - SPSS data files
  • R Data - R RDS and RData files
  • fst - R fst columnar on-disk frames (fst extra; experimental)
  • PX - PC-Axis format
  • ARFF - Attribute-Relation File Format (Weka format)
  • LIBSVM - Sparse labeled feature vectors (read & write)
  • NumPy - .npy / .npz array rows (read & write; npy extra)
Scientific Formats
  • NetCDF - Network Common Data Form for scientific data
  • CDF - NASA Common Data Format (space science)
  • HDF5 - Hierarchical Data Format
  • Zarr - Chunked array stores (zarr extra; experimental)
  • XYZ - Molecular/point coordinate tables
  • CIF - Crystallographic Information File (atom_site loops; experimental)
  • PDB - Protein Data Bank ATOM/HETATM records
  • MATLAB MAT - MATLAB .mat variables (mat extra; experimental)
  • SEG-Y - Seismic traces (geophysical extra; experimental)
  • GRIB2 - Meteorological messages (geophysical extra; experimental)
  • miniSEED - Seismological waveform windows (geophysical extra; experimental)
Geospatial Formats
  • GeoJSON - Geographic JSON format
  • GeoJSON Text Sequence - RFC 8142 line-delimited GeoJSON Features (.geojsonl, .geojsons); streaming-friendly
  • GeoPackage - OGC GeoPackage format
  • GML - Geography Markup Language
  • KML - Keyhole Markup Language
  • KMZ - KML Zipped (ZIP archive containing KML)
  • GPX - GPS Exchange Format (waypoints, routes, tracks)
  • Shapefile - ESRI Shapefile format
  • File Geodatabase - ESRI FileGDB layers via Fiona (geospatial extra; experimental)
  • MapInfo MIF - MapInfo Interchange Format (geospatial extra; experimental)
  • Esri ASCII Grid - Raster grids as cell or row records (.asc)
  • ArcInfo E00 - Interchange exports (experimental subset)
  • LAS - LiDAR point clouds (lidar extra; experimental)
  • BAG - Bathymetric Attributed Grid (hdf5 extra; experimental)
  • CZML - Cesium CZML document packets
  • FlatGeobuf - Streaming geospatial features with optional spatial-index filtering
  • MVT/PBF - Mapbox Vector Tiles
  • TopoJSON - Topology-preserving GeoJSON extension
RDF & Semantic Formats
  • JSON-LD - JSON for Linking Data
  • RDF/XML - RDF in XML format
  • Turtle - Terse RDF Triple Language
  • N-Triples - Line-based RDF format
  • N-Quads - N-Triples with context
  • TriG - RDF Triple Graph format
  • N3 - Notation3 RDF format
  • TriX - XML Triple RDF format
  • HDT - Header-Dictionary-Triples compact RDF (rdf extra; experimental)
Feed Formats
  • Atom - Atom Syndication Format
  • RSS - Rich Site Summary feed format
Network Formats
  • PCAP - Packet Capture format
  • PCAPNG - PCAP Next Generation format
Log & Event Formats
  • Apache Log - Apache access/error logs
  • CEF - Common Event Format
  • GELF - Graylog Extended Log Format
  • WARC - Web ARChive format
  • CDX - Web archive index format
  • ILP - InfluxDB Line Protocol
  • HTML - HTML files with table extraction
Email Formats
  • EML - Email message format
  • MBOX - Mailbox format
  • MHTML - MIME HTML format
Configuration Formats
  • INI - INI configuration files
  • TOML - Tom's Obvious Minimal Language
  • YAML - YAML Ain't Markup Language
  • HOCON - Human-Optimized Config Object Notation
  • EDN - Extensible Data Notation
Office Formats
  • XLS/XLSX - Microsoft Excel files
  • XLSB - Excel Binary format
  • ODS - OpenDocument Spreadsheet
  • Microsoft Access - Access .mdb / .accdb tables (access extra; experimental)
  • Lotus 1-2-3 - Legacy WK1 / .123 spreadsheets (experimental)
Business & Exchange Formats
  • EDI - X12 / EDIFACT segment streams (experimental)
  • IATI - Aid-transparency activity XML (xml extra; experimental)
CAD Formats
  • DXF - AutoCAD Drawing Exchange Format
Graph Formats
  • GraphML - Graph Markup Language
  • GEXF - Graph Exchange XML Format
  • DOT - GraphViz DOT format
Sequence & Alignment Formats
  • FASTA - Sequence format (protein/nucleotide)
  • FASTQ - Sequence with quality format
  • SAM - Sequence Alignment/Map (text)
  • BAM - Binary SAM format
  • Genomic VCF/BCF - Variant Call Format for genomic data (distinct from vCard .vcf; requires bio extra)
  • CRAM - Reference-compressed sequence alignments (requires alignment extra and an explicit reference when needed)
  • BED - BED3–BED12 genomic intervals
  • GFF3/GTF - Genomic feature annotations with coordinate and attribute preservation
Streaming & Big Data Formats
  • Kafka - Apache Kafka format
  • Pulsar - Apache Pulsar format
  • Flink - Apache Flink format
  • Beam - Apache Beam format
  • RecordIO - RecordIO format
  • SequenceFile - Hadoop SequenceFile
  • TFRecord - TensorFlow Record format
  • WebDataset - TAR shards grouped into ML sample dicts (format="webdataset"; experimental)
Protocol & Serialization Formats
  • Protocol Buffers - Google Protocol Buffers
  • Cap'n Proto - Cap'n Proto serialization
  • FlatBuffers - FlatBuffers serialization
  • FlexBuffers - FlexBuffers format
  • Thrift - Apache Thrift format
  • ASN.1 - ASN.1 encoding format
  • Ion - Amazon Ion format
  • OTLP JSON/Protobuf - OpenTelemetry traces, logs, and metrics export profiles (requires otlp extra)
Other Formats
  • TAR - Multi-file archive container (read-only; streams members without extracting to disk)
  • vCard (VCF) - Electronic business cards (RFC 6350); not genomic Variant Call Format
  • iCal - iCalendar format
  • LDIF - LDAP Data Interchange Format
  • TXT - Plain text files

See the formats documentation (or docs/docs/formats/ in this repo) for per-format parameters, record shapes, and extras.

Supported Compression Codecs

  • GZip (.gz)
  • BZip2 (.bz2)
  • LZMA (.xz, .lzma)
  • LZ4 (.lz4)
  • ZIP (.zip)
  • Brotli (.br)
  • ZStandard (.zst, .zstd)
  • Snappy (.snappy, .sz) — streaming decompression for framed files
  • LZO (.lzo, .lzop) — streaming decompression with legacy blob fallback
  • 7z (.7z; requires py7zr via iterabledata[compression])

Requirements

Python 3.10+

Installation

pip install iterabledata

The PyPI package is iterabledata. Import iterable:

from i

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