"Understanding and Using JSON" matching MCP servers:

Lets agents and CLI users inspect the shape of large JSON and JSONL files and extract exactly the values they need using jq-like expressions, returning compact bounded results instead of flooding context.

Feedback collection for AI agents. Create surveys from JSON schema, collect responses from groups of people, and retrieve structured results.

Enables parsing PDF files from local paths or URLs into structured JSON or Markdown format using NetMind's AI-powered PDF extraction service.

An MCP server that enables MCP clients to directly access and process arXiv papers by fetching the LaTeX source, improving mathematical content understanding.

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

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.

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 running Simulatte's 26 research SKUs (concept testing, pricing, etc.) from any MCP-compatible AI client, returning structured JSON results.

Generate academic research papers from any topic using the OpenPaper API. Retrieves real citations from academic databases and produces citation-grounded papers in PDF, DOCX, and structured JSON formats.

Enables reading and understanding NetCDF files, providing tools to inspect structure, variables, metadata, and data quality for scientific datasets.

Enables MCP clients to search Reddit, scrape post URLs with full comment threads, and pull bulk subreddit posts as structured JSON using Apify, without needing a Reddit API key or login.

A NestJS MCP server that extracts tables from documents (PDF, etc.) into a single JSON file using OpenRouter's Claude Haiku model.

An MCP-native pipeline for collecting structured intelligence on higher education institutions using the WHED schema, enabling scraping, extraction, validation, and saving of profiles.

Enables AI agents to query historical market context data from the Konseki API, returning raw JSON for analysis.

MCP server that extracts structured JSON from public URLs for AI agents using schemas like product, article, and company.

Provides comprehensive financial data and analysis tools using Yahoo Finance, including real-time stock data, historical data with technical indicators, options chains, and financial statements.

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.

Automates competitive research for startup ideas by executing a 7-step pipeline including idea understanding, competitor discovery, profile extraction, feature comparison, market gap analysis, innovation scoring, and report generation.

An MCP server that provides a toolbox for interacting with EPA SWMM stormwater models, enabling users to analyze model data and interpret results through LLM-driven tools. It assists stormwater modelers in understanding hydraulic systems and modeling behavior using natural language interfaces.

A memory-efficient MCP server for analyzing large JSON files with streaming, querying, schema detection, and chunk processing.