"Understanding File Systems" matching MCP servers:

Enables natural-language search over locally indexed files such as markdown, text, images, videos, and PDFs, and retrieves indexed text or media metadata by path. It lets Cursor query a local embedding index built with Gemini and SQLite.

Provides brain-inspired long-term memory for MCP clients, enabling persistent user understanding across sessions with Chinese-optimized hybrid retrieval and evolving user profiles.

MCP server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.

A dedicated server that wraps Google's Gemini AI models in a Model Context Protocol (MCP) interface, allowing other LLMs and MCP-compatible systems to access Gemini's capabilities like content generation, function calling, chat, and file handling through standardized tools.

AI-powered agentic workspace by Ambuj Systems for career intelligence, resume tailoring, and claim verification.

Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

MCP Compass is a discovery and recommendation service that assists AI assistants in finding and understanding Model Context Protocol servers through natural language queries.

A server that enables LLMs like Claude to interact with Azure Cosmos DB databases through natural language queries, acting as a translator between AI assistants and database systems.

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.

Provides seamless access to Grok AI's capabilities (chat completion, image understanding, and function calling) directly from Cline via the Model Context Protocol.

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.

Pre-trade risk validation and position sizing for AI trading agents via G-formula and Iron Fist.

Enables AI assistants to access contextual knowledge from multiple repositories through Sequa's Contextual Knowledge Engine. Provides architecture-aware code understanding and cross-repository context for more accurate, production-ready code generation.

A Model Context Protocol server that enhances AI agents by providing deep semantic understanding of codebases, enabling more intelligent interactions through advanced code search and contextual awareness.

Exposes Dropbox Dash search and file metadata via STDIO, enabling users to search across company content and fetch detailed file metadata and content.

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.

An MCP server that gives AI agents structured code understanding and precise code intelligence via local indexing of AST, call graphs, and semantic search.

The Wanaku MCP Router stands between autonomous AI agents and your enterprise systems. Wanaku leverages proven integration technology, like Apache Camel, to set up and manage hundreds or thousands of integrations.

Enables read-only querying of a verified knowledge library through an API and MCP interface, letting people and agents ask questions about systems discovery findings.