MCP Server that interacts with Azure AI Foundry (experimental)

A Model Context Protocol server for Azure AI Foundry, providing a unified set of tools for models, knowledge, evaluation, and more.

Available Tools

Capabilities: Models

Category	Tool	Description
Explore	list_models_from_model_catalog	Retrieves a list of supported models from the Azure AI Foundry catalog.
	list_azure_ai_foundry_labs_projects	Retrieves a list of state-of-the-art AI models from Microsoft Research available in Azure AI Foundry Labs.
	get_model_details_and_code_samples	Retrieves detailed information for a specific model from the Azure AI Foundry catalog.
Build	get_prototyping_instructions_for_github_and_labs	Provides comprehensive instructions and setup guidance for starting to work with models from Azure AI Foundry and Azure AI Foundry Labs.
Deploy	get_model_quotas	Get model quotas for a specific Azure location.
	create_azure_ai_services_account	Creates an Azure AI Services account.
	list_deployments_from_azure_ai_services	Retrieves a list of deployments from Azure AI Services.
	deploy_model_on_ai_services	Deploys a model on Azure AI Services.
	create_foundry_project	Creates a new Azure AI Foundry project.

Capabilities: Knowledge

Category	Tool	Description
Index	list_index_names	Retrieve all names of indexes from the AI Search Service
	list_index_schemas	Retrieve all index schemas from the AI Search Service
	retrieve_index_schema	Retrieve the schema for a specific index from the AI Search Service
	create_index	Creates a new index
	modify_index	Modifies the index definition of an existing index
	delete_index	Removes an existing index
Document	add_document	Adds a document to the index
	delete_document	Removes a document from the index
Query	query_index	Searches a specific index to retrieve matching documents
	get_document_count	Returns the total number of documents in the index
Indexer	list_indexers	Retrieve all names of indexers from the AI Search Service
	get_indexer	Retrieve the full definition of a specific indexer from the AI Search Service
	create_indexer	Create a new indexer in the Search Service with the skill, index and data source
	delete_indexer	Delete an indexer from the AI Search Service by name
Data Source	list_data_sources	Retrieve all names of data sources from the AI Search Service
	get_data_source	Retrieve the full definition of a specific data source
Skill Set	list_skill_sets	Retrieve all names of skill sets from the AI Search Service
	get_skill_set	Retrieve the full definition of a specific skill set
Content	fk_fetch_local_file_contents	Retrieves the contents of a local file path (sample JSON, document etc)
	fk_fetch_url_contents	Retrieves the contents of a URL (sample JSON, document etc)

Capabilities: Evaluation

Category	Tool	Description
Evaluator Utilities	list_text_evaluators	List all available text evaluators.
	list_agent_evaluators	List all available agent evaluators.
	get_text_evaluator_requirements	Show input requirements for each text evaluator.
	get_agent_evaluator_requirements	Show input requirements for each agent evaluator.
Text Evaluation	run_text_eval	Run one or multiple text evaluators on a JSONL file or content.
	format_evaluation_report	Convert evaluation output into a readable Markdown report.
Agent Evaluation	agent_query_and_evaluate	Query an agent and evaluate its response using selected evaluators. End-to-End agent evaluation.
	run_agent_eval	Evaluate a single agent interaction with specific data (query, response, tool calls, definitions).
Agent Service	list_agents	List all Azure AI Agents available in the configured project.
	connect_agent	Send a query to a specified agent.
	query_default_agent	Query the default agent defined in environment variables.

Capabilities: Finetuning

Category	Tool	Description
Finetuning	fetch_finetuning_status	Retrieves detailed status and metadata for a specific fine-tuning job, including job state, model, creation and finish times, hyperparameters, and any errors.
	list_finetuning_jobs	Lists all fine-tuning jobs in the resource, returning job IDs and their current statuses for easy tracking and management.
	get_finetuning_job_events	Retrieves a chronological list of all events for a specific fine-tuning job, including timestamps and detailed messages for each training step, evaluation, and completion.
	get_finetuning_metrics	Retrieves training and evaluation metrics for a specific fine-tuning job, including loss curves, accuracy, and other relevant performance indicators for monitoring and analysis.
	list_finetuning_files	Lists all files available for fine-tuning in Azure OpenAI, including file IDs, names, purposes, and statuses.
	execute_dynamic_swagger_action	Executes any tool dynamically generated from the Swagger specification, allowing flexible API calls for advanced scenarios.
	list_dynamic_swagger_tools	Lists all dynamically registered tools from the Swagger specification, enabling discovery and automation of available API endpoints.

Prompt Examples

Models

Explore models

• How can you help me find the right model?
• What models can I use from Azure AI Foundry?
• What OpenAI models are available in Azure AI Foundry?
• What are the most popular models in Azure AI Foundry? Pick me 10 models.
• What models are good for reasoning? Show me some examples in two buckets, one for large models and one for small models.
• Can you compare Phi models and explain differences?
• Show me the model card for Phi-4-reasoning.
• Can you show me how to test a model?
• What does free playground in Azure AI Foundry mean?
• Can I use GitHub token to test models?
• Show me latest models that support GitHub token.
• Who are the model publishers for the models in Azure AI Foundry?
• Show me models from Meta.
• Show me models with MIT license.

Build prototypes

• Can you describe how you can help me build a prototype using the model?
• Describe how you can build a prototype that uses an OpenAI model with my GitHub token. Don't try to create one yet.
• Recommend me a few scenarios to build prototypes with models.
• Tell me about Azure AI Foundry Labs.
• Tell me more about Magentic One
• What is Omniparser and what are potential use cases?
• Can you help me build a prototype using Omniparser?

Deploy OpenAI models

• Can you help me deploy OpenAI models?
• What steps do I need to take to deploy OpenAI models on Azure AI Foundry?
• Can you help me understand how I can use OpenAI models on Azure AI Foundry using GitHub token? Can I use it for production?
• I already have an Azure AI services resource. Can I deploy OpenAI models on it?
• What does quota for OpenAI models mean on Azure AI Foundry?
• Get me current quota for my AI services resource.

Quick Start with GitHub Copilot

This GitHub template has minimal setup with MCP server configuration and all required dependencies, making it easy to get started with your own projects.

This helps you automatically set up the MCP server in your VS Code environment under user settings. You will need uvx installed in your environment to run the server.

Manual Setup

1. Install uv by following Installing uv.

2. Start a new workspace in VS Code.

3. (Optional) Create .env file in the root of your workspace to set environment variables.

4. Create .vscode/mcp.json in the root of your workspace.

   {
       "servers": {
           "mcp_foundry_server": {
               "type": "stdio",
               "command": "uvx",
               "args": [
                   "--prerelease=allow",
                   "--from",
                   "git+https://github.com/azure-ai-foundry/mcp-foundry.git",
                   "run-azure-ai-foundry-mcp",
                   "--envFile",
                   "${workspaceFolder}/.env"
               ]
           }
       }
   }
   

5. Click Start button for the server in .vscode/mcp.json file.

6. Open GitHub Copilot chat in Agent mode and start asking questions.

See More examples for advanced setup for more details on how to set up the MCP server.

Setting the Environment Variables

To securely pass information to the MCP server, such as API keys, endpoints, and other sensitive data, you can use environment variables. This is especially important for tools that require authentication or access to external services.

You can set these environment variables in a .env file in the root of your project. You can pass the location of .env file when setting up MCP Server, and the server will automatically load these variables when it starts.

See example .env file for a sample configuration.

Category	Variable	Required?	Description
Model	GITHUB_TOKEN	No	GitHub token for testing models for free with rate limits.
Knowledge	AZURE_AI_SEARCH_ENDPOINT	Always	The endpoint URL for your Azure AI Search service. It should look like this: https://<your-search-service-name>.search.windows.net/.
	AZURE_AI_SEARCH_API_VERSION	No	API Version to use. Defaults to 2025-03-01-preview.
	SEARCH_AUTHENTICATION_METHOD	Always	service-principal or api-search-key.
	AZURE_TENANT_ID	Yes when using service-principal	The ID of your Azure Active Directory tenant.
	AZURE_CLIENT_ID	Yes when using service-principal	The ID of your Service Principal (app registration)
	AZURE_CLIENT_SECRET	Yes when using service-principal	The secret credential for the Service Principal.
	AZURE_AI_SEARCH_API_KEY	Yes when using api-search-key	The API key for your Azure AI Search service.
Evaluation	EVAL_DATA_DIR	Always	Path to the JSONL evaluation dataset
	AZURE_OPENAI_ENDPOINT	Text quality evaluators	Endpoint for Azure OpenAI
	AZURE_OPENAI_API_KEY	Text quality evaluators	API key for Azure OpenAI
	AZURE_OPENAI_DEPLOYMENT	Text quality evaluators	Deployment name (e.g., gpt-4o)
	AZURE_OPENAI_API_VERSION	Text quality evaluators	Version of the OpenAI API
	AZURE_AI_PROJECT_ENDPOINT	Agent services	Used for Azure AI Agent querying and evaluation

[!NOTE] Model

• GITHUB_TOKEN is used to authenticate with GitHub API for testing models. It is not required if you are exploring models from Foundry catalog.

Knowledge

• See Create a search service to learn more about provisioning a search service.
• Azure AI Search supports multiple authentication methods. You can use either a Microsoft Entra authentication or an Key-based authentication to authenticate your requests. The choice of authentication method depends on your security requirements and the Azure environment you are working in.
• See Authenication to learn more about authentication methods for a search service.

Evaluation

• If you're using agent tools or safety evaluators, make sure the Azure project credentials are valid.
• If you're only doing text quality evaluation, the OpenAI endpoint and key are sufficient.

License

MIT License. See LICENSE for details.