Yes, Model Context Protocol (MCP) servers can be connected to databases or file systems, though the implementation details depend on the specific MCP framework and the tools it supports. MCP is designed to manage machine learning models and their contexts, which often requires interaction with external data sources for tasks like storing model metadata, loading training datasets, or saving inference results. To achieve this, developers typically use built-in connectors, custom scripts, or middleware to bridge the MCP server with databases (SQL, NoSQL) or file systems (local, cloud-based, or distributed).
For example, an MCP server might use a Python library like SQLAlchemy or psycopg2 to connect to a PostgreSQL database. This allows the server to query metadata about deployed models, such as version history or performance metrics, and store it in relational tables. Similarly, for file systems, MCP could leverage cloud storage SDKs (e.g., AWS SDK for S3 or Google Cloud Storage client) to retrieve training data stored in object storage. In on-premises setups, the server might access shared network drives or Hadoop Distributed File System (HDFS) paths to read or write files. These integrations are often handled via configuration files or environment variables that define connection strings, credentials, or access keys.
However, developers must consider security and scalability when connecting MCP servers to external systems. Database connections require proper authentication (e.g., username/password, IAM roles) and encryption (TLS/SSL) to protect sensitive data. For file systems, access controls (e.g., bucket policies in S3) and efficient chunking/streaming mechanisms are critical, especially when handling large datasets. Additionally, asynchronous operations—like queuing data writes via message brokers (RabbitMQ, Kafka)—can prevent the MCP server from becoming a bottleneck during high-throughput scenarios. By designing these integrations thoughtfully, developers ensure that MCP servers remain performant and secure while leveraging existing data infrastructure.