"Files formatted in JSON" matching MCP servers:

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.

The Excel to JSON MCP provides a standardized interface for converting Excel and CSV data into JSON format using the Model Context Protocol.

The JSON to Excel MCP provides a standardized interface for converting (1)JSON data (2)URL pointing to JSON files into CSV format

json-mcp-server

Enables AI to create, edit, and batch generate JSON data with advanced rule engines. Supports CRUD operations, node-level editing, template management, and multi-format file exports (JSON, JSONL, CSV).

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.

Enables natural language interaction with JSON files, providing tools to load, query, aggregate, transform, and export data directly from AI editors.

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.

A lightweight, powerful, and flexible workflow engine that executes tasks defined in YAML configuration files.

Provides safe, deterministic inspection, transformation, validation, and diffing of structured data (JSON, CSV, YAML, Parquet) via schema-aware MCP tools.

Convert PDFs to structured JSON. Extract invoices, bank statements, contracts, and more. Pay per call via x402 USDC.

Offline-first MCP server for TwitterAPI.io docs (endpoints, pages, blogs) with fast fuzzy search.

Converts, aligns, reshapes, and inspects tabular data across Markdown, CSV, TSV, JSON, and HTML with CJK-aware alignment and deterministic transformations, entirely offline and without file access.

Converts PDF, DOCX, PPTX, XLSX, CSV, and JSON into clean, compact, AI-ready Markdown, reducing tokens up to 65%.

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.

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.

Read-only SQL across PostgreSQL, MySQL, S3-compatible buckets and folders of Parquet, CSV and JSON files — and one query can join across all of them, federated in-process by DuckDB. Sources are given as connection strings at startup, and no tool writes: they are absent rather than disabled.

Parses NACHA/ACH files into structured JSON and summaries, performing structural and arithmetic validation with IAT support, enabling automated file inspection and validation through MCP clients.

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.

Enables fetching any public web page through managed proxies, with optional JavaScript rendering, returning markdown, text, HTML, or structured JSON.