"Understanding File Usage" 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.

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.

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

Manages LinkedIn prospecting CSV files with deduplication and search, reducing LLM token usage by offloading file operations.

Enables minimal interaction with Supabase databases through 4 essential tools for data querying, mutations, file storage, and user authentication. Designed for 70% less context usage than standard implementations with auto-truncated results and simplified parameters.

Enables natural language analysis of Azure usage data from CSV files, providing cost summaries, visualizations, and insights about service and regional spending patterns.

A read-only DuckDB MCP server offering context-efficient analytics tools (list_datasets, describe_table, profile_column, explain, query) with a semantic layer for business rules, security guards, and disclosed truncation to help LLMs produce correct answers while minimizing token usage.

Provides read-only visibility into Snowflake warehouse cost and query performance, including credit usage by warehouse, expensive queries, and heuristic right-sizing recommendations.

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

Exposes 3 meta-tools to efficiently search and execute 174 backend CRUD tools, reducing context token usage by ~98%.

A read-only MCP server that exposes SQL database access to LLMs, supporting multiple database types, compact columnar results, pagination, and file export.

Read-only MCP server for searching your local Retrace screen-history database, with tools for full-text search, segment listing, frame details, app usage, and tags.

MCP server for tabular data retrieval that indexes local CSV, Excel, Parquet, and JSONL files once and answers questions via column profiles, filtered rows, server-side aggregations, and joins, drastically reducing token usage for large datasets.

Context-efficient MCP server that executes OpenSearch DSL queries and returns synthesized output instead of raw JSON, reducing token usage by up to 90%.

Provides AI assistants with unified file operations via MCP, supporting Excel, PDF, Word, and SQLite file reading, writing, and querying.

heroicons-mcp is a Model Context Protocol (MCP) server that exposes Heroicons. It allows LLMs and agentic applications to search, list, and generate usage examples of icons in React applications.

Enables efficient extraction of specific data from JSON APIs using JSONPath patterns, reducing token usage by up to 99% compared to fetching entire responses. Supports single and batch operations for both JSON extraction and raw text retrieval from URLs.

An MCP server for tracking and managing AI command usage history using a PostgreSQL database. It enables users to log, search, and view statistics for various AI-related commands and their execution contexts.

A comprehensive MCP server for Dokploy that provides 14 action-based tools covering the full DevOps lifecycle with minimal token usage. It enables AI assistants to manage projects, applications, databases, domains, servers, and more through a unified interface.

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.