AZURE AI FOUNDRY MCP SERVER TOOLS

48 tools from the Azure AI Foundry MCP Server MCP Server, categorised by risk level.

View the Azure AI Foundry MCP Server policy →

// ALL 48 AZURE AI FOUNDRY MCP SERVER TOOLS

READ 33 tools

Read agent_query_and_evaluate agent_query_and_evaluate Read fetch_finetuning_status Fetches the status of a fine-tuning job using Azure OpenAI API. Read fk_fetch_local_file_contents Reads the content of a local file and returns it as a string Read fk_fetch_url_contents Fetches the contents of the given HTTP URL Read format_evaluation_report Format evaluation results into a readable report with metrics and Studio URL. Read get_agent_evaluator_requirements Get the required input fields for a specific agent evaluator or all agent evaluators. Read get_data_source Retrieves the details of a specific data source by name Read get_document_count Return the total number of documents in the index Read get_finetuning_job_events MCP-compatible function to retrieve all events for a specific finetuning job. Read get_finetuning_metrics Retrieves fine-tuning metrics if the job has succeeded. Read get_indexer Retrieves the details of a specific indexer by name. Read get_model_details_and_code_samples get_model_details_and_code_samples Read get_model_quotas get_model_quotas Read get_prototyping_instructions_for_github_and_labs get_prototyping_instructions_for_github_and_labs Read get_skill_set Retrieves the details of a specific skill set by name Read get_text_evaluator_requirements Get the required input fields for a specific text evaluator or all text evaluators. Read list_agent_evaluators Returns a list of available agent evaluator names for evaluating agent behaviors. Read list_agents List available agents in the Azure AI Agent Service. Read list_azure_ai_foundry_labs_projects list_azure_ai_foundry_labs_projects Read list_data_sources Retrieves the list of all data source names Read list_deployments_from_azure_ai_services list_deployments_from_azure_ai_services Read list_dynamic_swagger_tools list_dynamic_swagger_tools Read list_finetuning_files Lists all files available for fine-tuning in Azure OpenAI. Read list_finetuning_jobs MCP-compatible function to list all finetuning jobs using Azure OpenAI API. Read list_index_names Retrieves the names of all indexes Read list_index_schemas Retrieves the schemas for all indexes Read list_indexers Retrieves the list of all the names of the indexers Read list_models_from_model_catalog list_models_from_model_catalog Read list_skill_sets Retrieves the list of the names of all skill sets Read list_text_evaluators Returns a list of available text evaluator names for evaluating text outputs. Read query_default_agent Send a query to the default configured Azure AI Agent. Read query_index Search a specific index for documents in that index Read retrieve_index_schema Retrieves the schema for a specific index

WRITE 8 tools

Write add_document Adds a document to the index Write connect_agent Connect to a specific Azure AI Agent and run a query. Write create_azure_ai_services_account create_azure_ai_services_account Write create_foundry_project create_foundry_project Write create_index Creates an AI Search index Write create_indexer Creates a new indexer Write modify_index Updates an AI Search index with a new index definition Write update_model_deployment update_model_deployment

DESTRUCTIVE 3 tools

Destructive delete_document Removes a document from the index Destructive delete_index Deletes the specified index Destructive delete_indexer Deletes the indexer

EXECUTE 4 tools

Execute deploy_model_on_ai_services deploy_model_on_ai_services Execute execute_dynamic_swagger_action Execute a dynamically generated tool from the Swagger specification. Execute run_agent_eval run_agent_eval Execute run_text_eval run_text_eval

// FAQ

How many tools does the Azure AI Foundry MCP Server MCP server have? +

The Azure AI Foundry MCP Server MCP server exposes 48 tools across 4 categories: Read, Write, Destructive, Execute.

How do I enforce policies on Azure AI Foundry MCP Server tools? +

Route the Azure AI Foundry MCP Server server through the PolicyLayer gateway. Define allow, deny, or approval rules per tool in the dashboard; they are enforced on every call before it reaches the server.

What risk categories do Azure AI Foundry MCP Server tools fall into? +

Azure AI Foundry MCP Server tools are categorised as Read (33), Write (8), Destructive (3), Execute (4). Each category has a recommended default policy.

Enforce policy on every Azure AI Foundry MCP Server tool call.

Start from Azure AI Foundry MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

CHECK YOUR STACK →

Instant setup, no code required.

43,000+ MCP servers and 220,000+ tools scanned and risk-classified.