Unlocking AI-Driven Data Access: Azure Database for MySQL Support via the Azure MCP Server
Step into a new era of data-driven intelligence with the fusion of Azure MCP Server and Azure Database for MySQL, where your MySQL data is no longer just stored, but instantly conversational, intelligent and action-ready. By harnessing the open-standard Model Context Protocol (MCP), your AI agents can now query, analyze and automate in natural language, accessing tables, surfacing insights and acting on your MySQL-driven business logic as easily as chatting with a colleague. It’s like giving your data a voice and your applications a brain, all within Azure’s trusted cloud platform. We are excited to announce that we have added support for Azure Database for MySQL in Azure MCP Server. The Azure MCP Server leverages the Model Context Protocol (MCP) to allow AI agents to seamlessly interact with various Azure services to perform context-aware operations such as querying databases and managing cloud resources. Building on this foundation, the Azure MCP Server now offers a set of tools that AI agents and apps can invoke to interact with Azure Database for MySQL - enabling them to list and query databases, retrieve schema details of tables, and access server configurations and parameters. These capabilities are delivered through the same standardized interface used for other Azure services, making it easier to the adopt the MCP standard for leveraging AI to work with your business data and operations across the Azure ecosystem. Before we delve into these new tools and explore how to get started with them, let’s take a moment to refresh our understanding of MCP and the Azure MCP Server - what they are, how they work, and why they matter. MCP architecture and key components The Model Context Protocol (MCP) is an emerging open protocol designed to integrate AI models with external data sources and services in a scalable, standardized, and secure manner. MCP dictates a client-server architecture with four key components: MCP Host, MCP Client, MCP Server and external data sources, services and APIs that provide the data context required to enhance AI models. To explain briefly, an MCP Host (AI apps and agents) includes an MCP client component that connects to one or more MCP Servers. These servers are lightweight programs that securely interface with external data sources, services and APIs and exposes them to MCP clients in the form of standardized capabilities called tools, resources and prompts. Learn more: MCP Documentation What is Azure MCP Server? Azure offers a multitude of cloud services that help developers build robust applications and AI solutions to address business needs. The Azure MCP Server aims to expose these powerful services for agentic usage, allowing AI systems to perform operations that are context-aware of your Azure resources and your business data within them, while ensuring adherence to the Model Context Protocol. It supports a wide range of Azure services and tools including Azure AI Search, Azure Cosmos DB, Azure Storage, Azure Monitor, Azure CLI and Developer CLI extensions. This means that you can empower AI agents, apps and tools to: Explore your Azure resources, such as listing and retrieving details on your Azure subscriptions, resource groups, services, databases, and tables. Search, query and analyze your data and logs. Execute CLI and Azure Developer CLI commands directly, and more! Learn more: Azure MCP Server GitHub Repository Introducing new Azure MCP Server tools to interact with Azure Database for MySQL The Azure MCP Server now includes the following tools that allow AI agents to interact with Azure Database for MySQL and your valuable business data residing in these servers, in accordance with the MCP standard: Tool Description Example Prompts azmcp_mysql_server_list List all MySQL servers in a subscription & resource group "List MySQL servers in resource group 'prod-rg'." "Show MySQL servers in region 'eastus'." azmcp_mysql_server_config_get Retrieve the configuration of a MySQL server "What is the backup retention period for server 'my-mysql-server'?" "Show storage allocation for server 'my-mysql-server'." azmcp_mysql_server_param_get Retrieve a specific parameter of a MySQL server "Is slow_query_log enabled on server my-mysql-server?" "Get innodb_buffer_pool_size for server my-mysql-server." azmcp_mysql_server_param_set Set a specific parameter of a MySQL server to a specific value "Set max_connections to 500 on server my-mysql-server." "Set wait_timeout to 300 on server my-mysql-server." azmcp_mysql_table_list List all tables in a MySQL database "List tables starting with 'tmp_' in database 'appdb'." "How many tables are in database 'analytics'?" azmcp_mysql_table_schema_get Get the schema of a specific table in a MySQL database "Show indexes for table 'transactions' in database 'billing'." "What is the primary key for table 'users' in database 'auth'?" azmcp_mysql_database_query Executes a SELECT query on a MySQL Database. The query must start with SELECT and cannot contain any destructive SQL operations for security reasons. “How many orders were placed in the last 30 days in the salesdb.orders table?” “Show the number of new users signed up in the last week in appdb.users grouped by day.” These interactions are secured using Microsoft Entra authentication, which enables seamless, identity-based access to Azure Database for MySQL - eliminating the need for password storage and enhancing overall security. How are these new tools in the Azure MCP Server different from the standalone MCP Server for Azure Database for MySQL? We have integrated the key capabilities of the Azure Database for MySQL MCP server into the Azure MCP Server, making it easier to connect your agentic apps not only to Azure Database for MySQL but also to other Azure services through one unified and secure interface! How to get started Installing and running the Azure MCP Server is quick and easy! Use GitHub Copilot in Visual Studio Code to gain meaningful insights from your business data in Azure Database for MySQL. Pre-requisites Install Visual Studio Code. Install GitHub Copilot and GitHub Copilot Chat extensions. An Azure Database for MySQL with Microsoft Entra authentication enabled. Ensure that the MCP Server is installed on a system with network connectivity and credentials to connect to Azure Database for MySQL. Installation and Testing Please use this guide for installation: Azure MCP Server Installation Guide Try the following prompts with your Azure Database for MySQL: Azure Database for MySQL tools for Azure MCP Server Try it out and share your feedback! Start using Azure MCP Server with the MySQL tools today and let our cloud services become your AI agent’s most powerful ally. We’re counting on your feedback - every comment, suggestion, or bug-report helps us build better tools together. Stay tuned: more features and capabilities are on the horizon! Feel free to comment below or write to us with your feedback and queries at AskAzureDBforMySQL@service.microsoft.com.Lesson Learned #530: Comparing Execution Plans to Expose a Hidden Performance Anti-Pattern
One of the most powerful features of SSMS Copilot is how it lets you compare execution plans and immediately show you performance issues. In this case, I would like to share with you my lesson learned comparing two queries and how they behave very differently inside the engine. We have the following queries, these are using a table _x_y_z_MS_HighCPU that contains 4 millon of rows. The column TextToSearch is a varchar(200) datatype. -- Query 1 SELECT COUNT(*) FROM [MSxyzTest].[_x_y_z_MS_HighCPU] WHERE TextToSearch = N'Value: 9'; -- Query 2 SELECT COUNT(*) FROM [MSxyzTest].[_x_y_z_MS_HighCPU] WHERE TextToSearch = 'Value: 9'; Since the query texts are different, each will have a different query ID in Query Store. By running the following T-SQL, for example, I can identify the query IDs. SELECT qsqt.query_sql_text, qsq.query_id, qsp.plan_id, qsp.query_plan_hash, qsp.last_execution_time FROM sys.query_store_query_text qsqt JOIN sys.query_store_query qsq ON qsqt.query_text_id = qsq.query_text_id JOIN sys.query_store_plan qsp ON qsq.query_id = qsp.query_id WHERE qsqt.query_sql_text LIKE '%SELECT COUNT(*)%' -- FROM [[MSxyzTest]].[[_x_y_z_MS_HighCPU]]%' ORDER BY qsp.last_execution_time DESC; Queries 1 and 2 can be compared directly. Using Copilot, I ran the following prompt: Compare the execution plans for the two queries (query id 1 and query id 2 using Query Store. Highlight any differences in operators, estimated vs actual row counts, or implicit conversions. Running the following prompt : CPU Usage: Please, show the top resource-consuming queries in the current database using Query Store data. Include query text, execution count, duration, CPU time, and logical reads. We could see the impact of using an antipattern:Lesson Learned #525: Tracking Command Timeouts in Azure SQL: Beyond Query Store with Extended Events
A few days ago, we were working on a support case where our customer was intermittently experiencing command timeouts. What made the case interesting was that queries which usually completed in under one second suddenly started taking more than 10 seconds to execute. Since the application — developed in Python using the ODBC Driver 18 for SQL Server — had a command timeout set to 5 seconds, the following error was triggered every time the threshold was exceeded: Error executing command, retrying in 5 seconds. Attempt 1 of 3 with new timeout 5. Error: ('HYT00', '[HYT00] [Microsoft][ODBC Driver 18 for SQL Server]Query timeout expired (0) (SQLExecDirectW)') The application had built-in retry logic, dynamically increasing the timeout in each of the three retry attempts, to allow time for the query to complete and to log enough data for post-error analysis. Example logs from the retry logic: (RunCommandTimeout) - Thread: 39808 - Error executing command, retrying in 5 seconds. Attempt 1 of 3 with new timeout 5. Error: ('HYT00', '[HYT00] [Microsoft][ODBC Driver 18 for SQL Server]Query timeout expired (0) (SQLExecDirectW)') INFO:root:Connecting to the DB jmjuradotestdb1 - Thread id 39808 - (Attempt 1/3) INFO:root:Connected to the Database in jmjuradotestdb1 - Thread id 39808 - 0.0445 seconds --- (RunCommandTimeout) - Thread: 39808 - Error executing command, retrying in 9 seconds. Attempt 2 of 3 with new timeout 9. Error: ('HYT00', '[HYT00] [Microsoft][ODBC Driver 18 for SQL Server]Query timeout expired (0) (SQLExecDirectW)') INFO:root:Connecting to the DB jmjuradotestdb1 - Thread id 39808 - (Attempt 1/3) INFO:root:Connected to the Database in jmjuradotestdb1 - Thread id 39808 - 0.0532 seconds --- (RunCommandTimeout) - Thread: 39808 - Error executing command, retrying in 13 seconds. Attempt 3 of 3 with new timeout 13. Error: ('HYT00', '[HYT00] [Microsoft][ODBC Driver 18 for SQL Server]Query timeout expired (0) (SQLExecDirectW)') (RunCommandTimeout) - Thread: 39808 - Loop:2/5 Execution Time: 9.7537 seconds My first prompt using SSMS Copilot was this "Review the queries that experienced a command timeout or were aborted in the last 30 minutes. Include query text, queryid, duration, and the reason and code for the abort if available." and I got the following results. So, all points that the query 216 got command timeouts. My next question, was, for query ID 216, show the number of total executions reporting that is 28 executions. The response showed 28 executions, but this number didn’t match the number of aborted and non-aborted executions observed in the application logs, why this difference? Checking the table sys.query_store_runtime_stats I found 10 rows all having execution_type = 3, and total executions 28. So, that's mean that Query Store aggregates query execution data over a fixed interval. So, the execution_type is an indicator that at least an execution during this runtime interval was aborted. So, at least several of them were aborted and other not. To obtain a more granular and accurate picture, I created an Extended Events session to capture these events using ring_buffer target. CREATE EVENT SESSION [CommandAborted] ON DATABASE ADD EVENT sqlserver.attention( ACTION ( sqlserver.client_app_name, sqlserver.client_hostname, sqlserver.username, sqlserver.database_name, sqlserver.sql_text ) ) ADD TARGET package0.ring_buffer WITH (MAX_MEMORY = 4096KB, EVENT_RETENTION_MODE = ALLOW_SINGLE_EVENT_LOSS); GO ALTER EVENT SESSION [CommandAborted] ON DATABASE STATE = START; after reproducing the command timeout scenario again, I was able to see only the aborted executions. So, in this case, 28 executions were executed and 7 executions were aborted. WITH RingBufferXML AS ( SELECT CAST(t.target_data AS XML) AS target_data FROM sys.dm_xe_database_session_targets t JOIN sys.dm_xe_database_sessions s ON t.event_session_address = s.address WHERE t.target_name = 'ring_buffer' AND s.name = 'CommandAborted' ) SELECT x.value('@name', 'varchar(50)') AS event_name, x.value('@timestamp', 'datetime2') AS event_time, x.value('(action[@name="client_app_name"]/value)[1]', 'nvarchar(256)') AS client_app_name, x.value('(action[@name="sql_text"]/value)[1]', 'nvarchar(max)') AS sql_text, x.value('(data[@name="duration"]/value)[1]', 'bigint') AS duration_microseconds, CAST(x.value('(data[@name="duration"]/value)[1]', 'bigint') / 1000000.0 AS decimal(10,3)) AS duration_seconds FROM RingBufferXML CROSS APPLY target_data.nodes('//event') AS tab(x) WHERE x.value('@name', 'varchar(50)') = 'attention' and x.value('(action[@name="client_app_name"]/value)[1]', 'nvarchar(256)') = 'TEST-DataCon' ORDER BY event_time DESC;Exploring AI Development and Management: A Journey through Contoso Chat and LLM Ops
In this blog, we'll navigate through the world of AI models, exploring Contoso Chat, Prompt Engineering, limitations of Prompt Engineering, and Large Language Models. We'll introduce tools like the RAG Pattern and Azure AI Studio that can boost AI responses and system performance. Ready to dive into the intricacies of AI development and management? Join us!Introduction to Prompt Engineering
With GPT-3, GPT-3.5, and GPT-4 prompt-based models, the user interacts with the model by entering a text prompt, to which the model responds with a text completion. Basic concepts and elements of GPT prompts Prompt components Instructions Primary Content Examples Cue Supporting content Prompts Basics Text prompts are how users interact with GPT models GPT models attempt to produce the next series of words that are most likely to follow from the previous text. Prompts | Best Practices Be Specific: Leave as little to interpretation as possible. Restrict the operational space Be Descriptive: Use analogies Double Down:Sometimes you may need to repeat yourself to the model. Give instructions before and after your primary content, use an instruction and a cue, etc. Order Matters:The order in which you present information to the model may impact the output. Whether you put instructions before your content (“summarize the following…”) or after (“summarize the above…”) can make a difference in output. Even the order of few-shot examples can matter. This is referred to as recency bias. Give the model an “out” :It can sometimes be helpful to give the model an alternative path if it is unable to complete the assigned task. For example, when asking a question over a piece of text you might include something like "respond with ‘not found’ if the answer is not present". This can help the model avoid generating false responses Prompt components Instructions When we show up to the present moment with all of our senses, we invite the world to fill us with joy. The pains of the past are behind us. The future has yet to unfold. But the now is full of beauty simply waiting for our attention. Instructions are likely the most commonly used prompt component Instructions - instruct the model on what to do Space efficiency TABLES As shown in the examples in the previous section, GPT models can understand tabular formatted data quite easily. This can be a space efficient way to include data, rather than preceding every field with name (such as with JSON). WHITE SPACE Consecutive whitespaces are treated as separate tokens which can be an easy way to waste space. Spaces preceding a word, on the other hand, are typically treated as part of the same token as the word. Carefully watch your usage of whitespace and don’t use punctuation when a space alone will do. Advanced techniques in prompt design and prompt engineering Certain models expect a specialized prompt structure For Azure OpenAI GPT models, there are currently two distinct APIs where prompt engineering comes into play: Chat Completion API Completion API Each API requires input data to be formatted differently Use of affordances | Factual claims, Search queries and Snippets Factual claims: John Smith is married to Lucy Smith John and Lucy have five kids John works as a software engineer at Microsoft Search queries: John Smith married to Lucy Smith John Smith number of children John Smith software engineer Microsoft Snippets: [1] … John Smith’s wedding was on September 25, 2012 … [2] … John Smith was accompanied by his wife Lucy to a party [3]John was accompanied to the soccer game by his two daughters and three sons [4] … After spending 10 years at Microsoft, Smith founded his own startup, Tailspin Toys [5] John M is the town smith, and he married Fiona. They have a daughter named Lucy System message framework and template recommendations for Large Language Models (LLMs) Define the model’s profile, capabilities, and limitations for your scenari Define the specific task(s) Define how the model should complete the tasks Define the scope and limitations Define the posture and tone Define the model's output format Define the language and syntax Define any styling or formatting Provide example(s) to demonstrate the intended behavior of the mode Describe difficult use cases Show the potential “inner monologue” Define additional behavioral guardrail Identify and prioritize the harms you’d like to address.How to Get Started with Copilot in Microsoft Dataverse
Discover the power of Microsoft Dataverse and Copilot in this comprehensive guide. Learn to leverage Copilot's capabilities for seamless table creation and modification. Explore UI differences, understand how to upload Excel files to Dataverse effortlessly, and gain insights into auditing company employee records. Unlock the full potential of these tools for streamlined workflows and efficient data management. Dive into our step-by-step tutorial now!GitHub Codespaces and GitHub Copilot: 2 stories, thousands of possibilities
The year 2022 marked a turning point for GitHub and all its users, with the announcement of the general availability of two revolutionary services for developers: GitHub Codespaces and GitHub Copilot. To give you a deeper understanding of what these services are and how you could benefit from them, let me tell you a story, or better two.
'Do you want to make this call' prompt
I have managed to set up click to call through chrome extensions, but I still get the prompt in teams ' Do you want to call this number?' every time I try to ring a number. Is there any way to remove this prompt and just have it call the number when I click it?
