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IterableData MCP server

by datenoio·io.github.datenoio/iterabledata·v1.1.0·38 stars
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Streaming read, convert, and inspect 100+ formats. Install iterabledata[mcp] and run iterable-mcp.

A86/100grade A
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Safety scan
A86/100

full report

Adoption
Established

38 stars1.3k downloads/wk

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IterableData tools (10)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • analyze_dataset

    Analyze dataset structure; optional AI documentation.

  • convert_file

    Convert between formats. Writes require confirm=True.

  • describe_capabilities

    Describe format metadata and capabilities.

  • detect_format

    Detect data format and compression for a file.

  • generate_documentation

    Generate AI-powered dataset documentation.

  • infer_schema

    Infer schema for a data file.

  • plan_conversion

    Produce a declarative conversion plan without writing files.

  • read_sample

    Read a bounded sample of rows from a data file.

  • suggest_transform

    Suggest a declarative transform spec (requires iterabledata[ai]).

  • translate_filter

    Translate a filter expression into a validated AST (DSL parsing; no LLM by default).

Public scan report

scanner v0.1.10 · 2026-11-03 · same rubric, same numbers if you re-run it

1 medium
  • Code scan260 source files scanned20/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 5 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year9/10

Findings (1)

  • mediumeval / new Function usedexec.eval
    iterabledata-1.1.0/iterable/ops/filter.py: …evaluation namespace. result = eval(code, {"__builtins__": {}}, safe_dict) …
Overall 86/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How IterableData compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
86/100
higher than 72% of 48,255 servers
Tools
10
the typical server has 7
Tools that change things
0%
typical: 0%. Fewer write tools means less an agent can break
GitHub stars
38
more than 93% of 28,688 servers
Downloads a week
1,256
more than 95% of 12,191 servers with a package

What its tools let an agent do: 9 read · 1 create

Release history: 2 versions in the registry since 2026-08-18, latest 2026-10-29, typically one every 72 days.

Install IterableData in Claude Code, Cursor or VS Code

claude mcp add iterabledata -- uvx iterabledata
Add to Cursor

What the publisher says

From the IterableData repository's README, as published. We do not edit it. Read it on GitHub

Iterable Data

<!-- mcp-name: io.github.datenoio/iterabledata -->

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

Supported File Types

Core Formats

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Grade history

  • 2026-11-03restoreB → Ascore 86: eval / new Function used

IterableData: common questions

Is IterableData MCP server safe?
Yes, by our scan: it is graded A (86/100). Read the IterableData safety report
How do I install IterableData?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does IterableData need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is IterableData maintained?
The last commit was 6 days ago (2026-10-29). The latest release is v1.1.0.
What can I use instead of IterableData?
Servers from other publishers that do the same job: Docling MCP server.
How does IterableData compare with other MCP servers?
safety score 86/100 (higher than 72% of 48,255 servers); tools 10 (the typical server has 7); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to IterableData

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