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"JSON File Upload, Data Vectorization, and LLM Similarity Search" matching MCP servers:

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    Upload-Post is the all-in-one social media MCP server the Model Context Protocol server for social media publishing, scheduling and analytics. Connect any MCP-compatible AI agent (Claude, Cursor, ChatGPT, Claude Desktop, n8n, and more) and publish, schedule and analyze content across 13+ social networks from one API: TikTok, Instagram, YouTube, YouTube Shorts, Instagram Reels, LinkedIn, Facebook
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    MIT
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    Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.
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    MIT
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    JSON Contracts is a local MCP server for transforming natural language into Git-controlled JSON contracts, JSON Schema validation, and structured LLM/agent outputs.
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    Apache 2.0
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    Provides tools to validate, format, and query JSON strings via MCP. Enables AI agents to work with JSON data without needing keys or internet.
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    MIT
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    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.
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    A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.
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    Apache 2.0
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    MCP server that provides a live coordination layer for AI agents, including attributable handoffs, a shared event ledger, atomic work-claiming, and advisory file leases to prevent collisions.
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    AGPL 3.0
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    MCP server toolkit with payload library, WAF bypass generator, rate limiter, plugin system, and inter-MCP bridge for security testing.
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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.
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    MIT
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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 efficient navigation and search of large JSON files (>10MB) through intelligent path exploration and fuzzy search capabilities, designed to save tokens by avoiding loading entire files into context.
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