"Understanding File Structures" matching MCP servers:

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.

Provides AI agents with data validation, transformation, and normalization capabilities, including JSON schema validation, CSV processing, data normalization, text cleaning, and dataset merging.

A tutorial implementation MCP server that enables analysis of CSV and Parquet files. Allows users to summarize data and query file information through natural language interactions.

Enables AI agents to load, query, and analyze IFC building model files, including spatial structures, elements, properties, materials, and geometry.

Provides efficient handling of large Excel files through automatic chunking and pagination, using MCP to enable seamless file reading and management features such as sheet selection and error handling.

Connects AI clients to the Matih data platform, enabling SQL queries, table profiling, analytics, chart creation, and file uploads through MCP tools with OAuth/PKCE authentication.

Enables AI applications to interact with decentralized storage through a standardized Model Context Protocol interface, allowing file uploads, retrievals, and identity management.

Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.

This read-only MCP Server allows you to connect to CSV Files data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for CSV Files (https://www.cdata.com/drivers/csv/download/mcp).

Enables DeepSeek Harness models to directly call local ArcGIS Pro via MCP tools for reading projects, layers, GDB structures, and executing Buffer/Clip/custom ArcPy operations locally.

Converts legacy supply chain databases and file drops into MCP servers for AI agents, enabling direct interaction with closed systems via CLI.

A Python-based MCP server that collects, analyzes, and visualizes forest fire occurrence data on maps, allowing users to access regional fire information, risk analysis, and map visualizations.

A tiny remote MCP server with a single tool, drop, that saves a file from local content or a URL into a mounted directory, acting as a file dropper for watch-folder importers.

This MCP server enables AI assistants to interact with a SQLite database through a spreadsheet-like interface, supporting operations such as querying, editing, importing Excel/CSV, and managing table structures via natural language.

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 read-only interaction with Snowflake databases through SQL queries, schema exploration, and data insight tracking. Provides tools to query data, list databases/schemas/tables, describe table structures, and maintain a memo of discovered insights.

Provides deterministic tools for understanding, transforming, and verifying structured data via MCP, enabling rule inference from examples and verification of transformed records.

Enables CRUD operations on NocoDB tables via natural language commands, supporting table manipulation, file upload, and bulk actions.

Exposes SAP BW/4HANA ADT operations as MCP tools for LLM clients, with a local-file buffering layer to handle large XML payloads and table data efficiently.

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.