"Understanding and Using JSON" matching MCP servers:

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 token-efficient, schema-aware MCP server that enables AI assistants to safely read, modify, query, and validate JSON, YAML, and TOML files with automatic schema detection and format conversion capabilities.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Local MCP tool for understanding JSON Schema files while planning tests, summarizing types, required fields, constraints, and generating placeholder paths without copying data.

Enables exploring Vedha access patterns through read-only, fixture-backed tools for listing contexts, retrieving canvas models, diagnoses, atlas reviews, and audits before connecting a production backend.

Query only the data you need from your JSON file and keep your context clean

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.

Turns any JSON file (catalogue, FAQ, price list) into an MCP server with three read-only tools: list with paging, keyword search across chosen fields, and get by id. Runs over stdio for Claude Desktop, Claude Code and Cursor.

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

Enables validating JSON/JSONC, getting/setting values with jq-like paths, pretty-printing or minifying, validating against Ajv JSON Schema, and diffing local files or inline strings without network calls.

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

A starter template for building MCP (Model Context Protocol) servers with TypeScript support. Provides a clean foundation with example tools, resources, and prompts for creating custom integrations with Claude, Cursor, or other MCP-compatible AI assistants.

A Model Context Protocol server that enables creating, modifying, and validating infinite canvas data structures according to the JSON Canvas 1.0 specification.

An MCP server that converts raw text prompts into structured JSON prompts using heuristic keyword extraction — no API keys required.

A powerful MCP server for querying and processing large Swagger/OpenAPI JSON documents, enabling LLMs to efficiently access API documentation without loading entire files.

JSON Contracts is a local MCP server for transforming natural language into Git-controlled JSON contracts, JSON Schema validation, and structured LLM/agent outputs.

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

A Model Context Protocol server for querying large JSON files using JSONPath expressions, enabling LLMs to efficiently search and extract information from large JSON data.

Enables efficient JSON file editing with targeted read, write, delete, and deep merge operations using dot notation paths, optimized for managing multilingual projects and large configuration files.

Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.