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

Enables agents to securely stage, validate, and submit binary data for on-premises storage, then list, inspect, process via registered server-side processors, and token-guarded delete those assets using opaque identifiers. Upload bytes travel over a separate data plane rather than through MCP JSON, with staging, validation, and prepare/apply/verify flows enforced throughout.

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.

MCP server for converting between everyday file formats (CSV, Excel, Markdown, Word), including a GxP-compliant Markdown to Word template. Exposes four conversion tools over stdio.

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.

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.

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

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

Converts JSON data to TOON (Token-Oriented Object Notation) format and back, reducing token usage by 30-60% for more efficient LLM applications.

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

Enables AI agents to convert CSV text into JSON arrays with automatic delimiter detection and optional header-row handling, accessed through a pay-per-call x402 micropayment API.

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.

Enables AI agents to discover, request access to, and query data products in Data Mesh Manager, enforcing governance policies while retrieving business data from platforms like Snowflake and Databricks.

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

An MCP server for dataset exploration and analysis, enabling LLM clients to perform summary, correlation, distribution, missing value analysis, data cleaning, and statistical tests directly on CSV files.

An MCP server for data reconciliation between MySQL and Snowflake, enabling LLM agents to validate data integrity during migrations and ETL processes.

Bootstrap MCP server for future data exploration and querying across multiple databases, currently only provides a hello_world tool with no actual data connectivity.

A governed MCP server enabling LLM agents to query BigQuery, inspect GCS, trigger Airflow DAGs, and run data-quality checks with built-in security guardrails like allow-lists, cost ceilings, and audit trails.