A Model Context Protocol server for Azure AI Foundry, providing a unified set of tools for models, knowledge, evaluation, and more.
Category Tool Description Explorelist_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. 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) 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. 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.
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
uvxinstalled in your environment to run the server.
Install uv by following Installing uv.
Start a new workspace in VS Code.
(Optional) Create .env file in the root of your workspace to set environment variables.
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"
]
}
}
}Click Start button for the server in .vscode/mcp.json file.
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 VariablesTo 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 ModelGITHUB_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
Evaluation
MIT License. See LICENSE for details.
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