mcp-azure-tablestorage
The Azure TableStore MCP Server enables interaction with Azure Table Storage through Cline, providing several capabilities:
Query Azure Storage Tables: Execute queries using OData filters with support for equality, compound conditions, numeric comparisons, and date comparisons. Results are limited to 5 items by default to protect the LLM's context window.
Get Table Schema: Retrieve property names and types of a specified table to understand its structure.
List All Tables: View all tables in the storage account, with optional prefix filtering.
Detailed Query Responses: Includes total matching items, applied limit, and a subset of items to manage LLM processing safely.
Simple Configuration: Set up using the
AZURE_STORAGE_CONNECTION_STRINGenvironment variable.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-azure-tablestorage query the Users table for active accounts"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Azure TableStore MCP Server
A TypeScript-based MCP server that enables interaction with Azure Table Storage directly through Cline. This tool allows you to query and manage data in Azure Storage Tables.
Features
Query Azure Storage Tables with OData filter support
Get table schemas to understand data structure
List all tables in the storage account
Detailed error handling and response information
Simple configuration through connection string
Related MCP server: Azure Omni-Tool MCP Server
Installation
Local Development Setup
Clone the repository:
git clone https://github.com/dkmaker/mcp-azure-tablestorage.git
cd mcp-azure-tablestorageInstall dependencies:
npm installBuild the server:
npm run buildNPM Installation
You can install the package globally via npm:
npm install -g dkmaker-mcp-server-tablestoreOr run it directly with npx:
npx dkmaker-mcp-server-tablestoreNote: When using npx or global installation, you'll still need to configure the AZURE_STORAGE_CONNECTION_STRING environment variable.
Installing in Cline
To use the Azure TableStore server with Cline, you need to add it to your MCP settings configuration. The configuration file is located at:
Windows: %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
Add the following to your configuration:
{
"mcpServers": {
"tablestore": {
"command": "node",
"args": ["C:/path/to/your/mcp-azure-tablestorage/build/index.js"],
"env": {
"AZURE_STORAGE_CONNECTION_STRING": "your_connection_string_here" // Required: Your Azure Storage connection string
}
}
}
}Replace C:/path/to/your/mcp-azure-tablestorage with the actual path where you cloned the repository.
Configuration
The server requires the following environment variable:
AZURE_STORAGE_CONNECTION_STRING: Your Azure Storage account connection string
Usage in Cline
⚠️ IMPORTANT SAFETY NOTE: The query_table tool returns a limited subset of results (default: 5 items) to protect the LLM's context window. DO NOT increase this limit unless explicitly confirmed by the user, as larger result sets can overwhelm the context window.
Once installed, you can use the Azure TableStore server through Cline. Here are some examples:
Querying a table:
Query the Users table where PartitionKey is 'ACTIVE'Cline will use the query_table tool with:
{
"tableName": "Users",
"filter": "PartitionKey eq 'ACTIVE'",
"limit": 5 // Optional: Defaults to 5 items. WARNING: Do not increase without user confirmation
}The response will include:
Total number of items that match the query (without limit)
Limited subset of items (default 5) for safe LLM processing
Applied limit value
For example:
{
"totalItems": 25,
"limit": 5,
"items": [
// First 5 matching items
]
}This design allows the LLM to understand the full scope of the data while working with a manageable subset. The default limit of 5 items protects against overwhelming the LLM's context window - this limit should only be increased when explicitly confirmed by the user.
Getting table schema:
Show me the schema for the Orders tableCline will use the get_table_schema tool with:
{
"tableName": "Orders"
}Listing tables:
List all tables in the storage accountCline will use the list_tables tool with:
{}Project Structure
src/index.ts: Main server implementation with Azure Table Storage interaction logicbuild/: Compiled JavaScript outputpackage.json: Project dependencies and scripts
Dependencies
@azure/data-tables: Azure Table Storage client library
@modelcontextprotocol/sdk: MCP server implementation toolkit
License
This project is licensed under the MIT License - see the LICENSE file for details. This means you can use, modify, distribute, and sublicense the code freely, provided you include the original copyright notice and license terms.
Available Tools
3 toolsget_table_schemaC
Get property names and types from a table
Inputs: tableName (required).
list_tablesC
List all tables in the storage account
Inputs: prefix.
query_tableA
⚠️ WARNING: This tool returns a limited subset of results (default: 5 items) to protect the LLM's context window. DO NOT increase this limit unless explicitly confirmed by the user.
Query data from an Azure Storage Table with optional filters.
Supported OData Filter Examples:
Simple equality: filter: "PartitionKey eq 'COURSE'" filter: "email eq 'user@example.com'"
Compound conditions: filter: "PartitionKey eq 'USER' and email eq 'user@example.com'" filter: "PartitionKey eq 'COURSE' and title eq 'GDPR Training'"
Numeric comparisons: filter: "age gt 25" filter: "costPrice le 100"
Date comparisons (ISO 8601 format): filter: "createdDate gt datetime'2023-01-01T00:00:00Z'" filter: "timestamp lt datetime'2024-12-31T23:59:59Z'"
Supported Operators:
eq: Equal
ne: Not equal
gt: Greater than
ge: Greater than or equal
lt: Less than
le: Less than or equal
and: Logical and
or: Logical or
not: Logical not
Inputs: tableName (required), filter, limit, select.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose with no overlap: get_table_schema retrieves metadata about table structure, list_tables enumerates available tables, and query_table fetches actual data from tables. The descriptions clearly differentiate these operations, making tool selection unambiguous for an agent.
All three tools follow a consistent verb_noun naming pattern (get_table_schema, list_tables, query_table) with perfect consistency in style and structure. The naming convention is predictable and follows the same grammatical pattern throughout the tool set.
With only 3 tools, this server feels somewhat thin for Azure Table Storage operations. While the tools cover basic read operations, the absence of create, update, or delete operations for tables or entities makes the surface incomplete for typical database workflows. The count is borderline minimal for the domain.
The tool set has significant gaps for a database/storage system. There are no tools for creating tables, inserting entities, updating entities, or deleting tables/entities - only read operations exist. While the query capabilities are well-documented, the lack of write operations creates dead ends for agents trying to perform complete data management workflows.
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