"Understanding File Systems" matching MCP servers:

An MCP server that provides persistent memory for database schemas by storing table structures and metadata in a local SQLite file. It enables LLMs to save, retrieve, and manage database snapshots to maintain a structured understanding of database architectures.

An MCP server providing access to XRootD file systems, allowing LLMs to browse directories, read file metadata, and access contents via the root:// protocol. It supports advanced features like campaign discovery, file searching, and ROOT file analysis for scientific data management.

Implements the Model Context Protocol (MCP) to provide AI models with a standardized interface for connecting to external data sources and tools like file systems, databases, or APIs.

Enables autonomous agents to securely access and manage virtual file systems, records, schemas, SQL and Mongo queries, and RAG search through scoped MCP tools, while supporting binary transfers via presigned S3 recipes.

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

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.

A comprehensive Model Context Protocol server implementation that enables AI assistants to interact with file systems, databases, GitHub repositories, web resources, and system tools while maintaining security and control.

Enables AI agents to access file systems, fetch web content, query databases, and execute shell commands through the standardized Model Context Protocol.

An MCP server that gives LLM clients access to a SQL Server database and the local filesystem, including file operations and image handling.

A read-only MCP server for querying parking management systems using natural language and secure SQL. It enables searching for parking locations, retrieving billing configurations, and understanding database schemas through business context tools.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

A single MCP server that exposes safe, permission-checked tools for AI assistants to reach file systems, databases, APIs, Git, cloud services, and business applications.

Enables AI assistants to manage Snipe-IT inventory systems through comprehensive asset and consumable operations. Supports creating, updating, tracking, and managing IT assets, consumables, maintenance records, file attachments, and generating labels.

Enables users to answer California drinking water questions about public water systems, SAFER risk/violation status, source-water chemistry, school lead sampling, and latest tap/treated-water results by ZIP code or water system ID, using curated Databricks Gold views over State Water Board open data.

Enables interaction with SAP S/4HANA systems via OData, allowing service discovery, metadata exploration, field value retrieval, and CRUD operations through natural language.

A Model Context Protocol server providing tools for DB queries, API calls, file I/O, and text transformations, enabling AI agents like Claude to perform real-world actions.

Marker-indexed SQL cache MCP server that indexes in-file @scry.\* markers into a SQLite database, enabling agents to query structured project knowledge via read-only SQL without LLM reasoning.

Enables AI assistants to interact with Hadoop Hue for executing SQL queries using Hive, SparkSQL, or Impala and managing HDFS files. It supports directory browsing, file transfers, and exporting query results to CSV through the Model Context Protocol.

Enables an AI assistant to read form structures and submissions from a Coletum account (read-only) and to produce Excel/CSV exports, Coletum-style or user-template PDFs, and photographic reports on request. It also stores preferences such as logo and company name and reports where each generated file was saved.

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