Azure TableStore MCP Server

The Azure TableStore MCP Server bridges the gap between conversational AI assistants and Azure’s NoSQL storage solution. By exposing Table Storage as a first‑class MCP, developers can let Claude or other LLMs read, write, and interrogate tabular data without leaving the chat interface. This is especially valuable for building data‑centric workflows where an assistant must retrieve recent metrics, audit logs, or user profiles on demand.

At its core, the server implements three lightweight tools: query_table, get_table_schema, and list_tables. The query tool supports OData filters, enabling precise data selection while automatically limiting the result set to a safe default of five rows. This safeguard protects the LLM’s context window and prevents accidental data overload. The schema tool exposes column definitions, giving the assistant a clear understanding of entity properties and types. Finally, list_tables offers a quick inventory of all tables in the account, facilitating dynamic discovery and navigation.

Developers can integrate this MCP into any Cline‑enabled workflow with minimal configuration—just supply the Azure Storage connection string as an environment variable. Once registered, the assistant can interpret natural‑language requests such as “Show me the schema for the Orders table” or “Query the Users table where PartitionKey is ‘ACTIVE’,” translating them into structured tool calls that return JSON payloads. The assistant can then summarize, analyze, or transform the data before presenting it to the user.

Real‑world scenarios include monitoring dashboards where an LLM fetches recent log entries, data‑driven decision support systems that pull customer metrics on demand, or automated compliance checks that query audit tables. The server’s OData support and schema introspection make it adaptable to both simple key‑value lookups and more complex query patterns, all while keeping the LLM’s memory footprint in check. By exposing Azure Table Storage through MCP, this server turns a cloud‑native datastore into an interactive knowledge source that can be queried, updated, and explored directly within the AI assistant’s conversational context.