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"Understanding and Using JSON" matching MCP servers:

  • F
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    Creates compact skeleton representations of large JSON files by preserving structure while truncating string values and deduplicating arrays, helping users understand JSON structure without processing the full data payload.
    1
    10
  • A
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    quality
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    An MCP server that provides tools for JSON validation, diffing, and transformation operations such as flattening and renaming. It also enables data format conversion between JSON, CSV, and YAML to streamline data processing for AI agents.
    40
    MIT
  • F
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    quality
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    Enables repair of malformed JSON strings into valid JSON for agent workflows by fixing common LLM formatting issues. Supports cleanup of code fences, smart quotes, unquoted keys, trailing commas, and missing brackets to ensure reliable data parsing.
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    Enables schema-aware exploration of JSON data by uploading samples, flattening nested structures, and using heuristic search with token overlap and fuzzy matching to find field paths for target names, accelerating ETL and API onboarding workflows.
  • A
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    Enables querying, inserting, and managing unstructured data in RawTree via SQL, JSON ingestion, and log inspection through MCP clients like Claude Code, Cursor, and Claude Desktop.
    12
    12
    1
    MIT
  • A
    license
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    quality
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    maintenance
    Enables LLMs to profile and analyze tabular data files (CSV, Parquet, Excel, JSON) by extracting schema, statistics, data quality issues, and dtype suggestions, returning structured JSON.
    4
    6
    2
    MIT
  • A
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    quality
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    maintenance
    Enables document processing with Suparse API, extracting structured data from documents into JSON, CSV, XLSX, or Google Sheets, and supports processing single files or folders.
    6
    30
    MIT
  • F
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    quality
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    Extracts structured JSON data from unstructured text using predefined schemas for receipts, invoices, resumes, and emails. It allows users to transform messy text into organized data through built-in or custom-defined fields.
    1
  • A
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    quality
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    maintenance
    Converts PDF bank statements into structured data (Markdown, JSON, CSV, JSONL) with verified transactions and balance checks, enabling agents to audit numbers.
    5
    16
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Turns a folder of CSV, Parquet, and JSON files into a single SQL-queryable source for AI agents, supporting JOINs across files with read-only sandboxed access.
    6
    2
    MIT
  • F
    license
    A
    quality
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    maintenance
    Enables efficient extraction of specific data from JSON APIs using JSONPath patterns, reducing token usage by up to 99% compared to fetching entire responses. Supports single and batch operations for both JSON extraction and raw text retrieval from URLs.
    4
    4
  • F
    license
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    quality
    F
    maintenance
    Enables users to process large datasets record by record, supporting JSON Lines and JSON with jq expressions, with stateful navigation, result tracking, and export capabilities.
    8