"Understanding File Systems" 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.

This MCP server enables database migration between heterogeneous systems (Oracle, PostgreSQL, SQL Server, Netezza to PostgreSQL or SQL Server) through AI assistants by providing tools for command preview, execution, validation, and workflow suggestions.

Resolves messy company, customer, and vendor names across CRM, ERP, and ITSM systems to canonical records using fuzzy matching, preventing AI agents from updating or creating wrong or duplicate records.

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.

MCP server that unifies real-time telemetry from industrial systems into a single queryable interface, enabling production visibility, anomaly detection, and operational insights.

Enables structured document understanding of local PDFs via a dual-extract MCP server, combining MinerU text/layout and Qwen3-VL vision with fusion adjudication for field extraction, tables, formulas, and validation. Fully local and offline.

Enables MCP clients to discover processor and controller service types, build and wire Apache NiFi 2.x process groups, schedule them, and debug queues and bulletins through the NiFi REST API. Supports flow spec application, automatic canvas layout, parameter contexts, and an optional read-only mode that blocks all writes.

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.

Enables comprehensive interaction with DHIS2 health information systems through 40+ tools covering complete Web API functionality. Supports data management, tracker programs, analytics, and bulk operations for DHIS2 development and administration.

An MCP server that enables Claude to train a linear regression model by simply uploading a CSV file, handling the entire ML pipeline from data preprocessing to model evaluation.

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 assistants to look up FIRE data standard fields, validate draft records, and receive mapping suggestions when mapping internal data formats onto FIRE.

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.

Enables AI assistants to interact with Deephaven real-time data systems, allowing natural language queries, script execution, and management of both Community Core and Enterprise deployments for live data analysis and streaming workflows.

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 AI agents to query live schema, lineage, and query-context across data warehouses, dbt projects, orchestration systems, and BI tools via MCP tools.

This MCP server enables AI-assisted professional profile generation by connecting to verified employee work data (skills, career history, certifications, projects) from employer HCM systems.

A server implementation of MCP for Apache Spark that provides query plans and catalog information to AI systems for query optimization and data discovery.

Enables AI agents to query permission-filtered world views, inspect evidence-backed signals, and trace timelines across integrated enterprise systems, handing deep investigation off to vertical MCP servers via verified capabilities.