The Azure MCP Server lets you manage Microsoft Foundry Extensions resources, including creating chat and text completions, generating embeddings, listing models, and working with knowledge indexes, with natural language prompts.
Microsoft Foundry is a platform for deploying and managing custom AI models in Azure. It provides tools and services for training, fine-tuning, deploying, and monitoring AI models in production environments.
Knowledge index commands require the endpoint of your Microsoft Foundry project. OpenAI commands require the resource name and resource group. The resource get command can list resources without providing a resource name.
Note
Tool parameters: The Azure MCP Server tools define parameters for data they need to complete tasks. Some of these parameters are specific to each tool and are documented here. Other parameters are global and shared by all tools. For more information, see Tool parameters.
Knowledge: List knowledge indexes
Get a list of knowledge indexes from Foundry:
- Find knowledge indexes created within Foundry projects.
- Use these indexes with AI agents for knowledge retrieval and RAG applications.
- The list updates as you create new indexes or update existing ones.
Example CLI command
azmcp foundryextensions knowledge index list \
--endpoint <endpoint>
| Parameter |
Type |
Required |
Description |
endpoint |
string |
Yes |
The endpoint URL for the Microsoft Foundry project or service. The endpoint follows this pattern: https://<foundry-resource-name>.services.ai.azure.com/api/projects/<project-name>. |
Example prompts include:
- View all indexes: "Show me all knowledge indexes at endpoint
https://my-example-resource.services.ai.azure.com/api/projects/my-project"
- Filter by project: "List knowledge indexes at endpoint
https://my-example-resource.services.ai.azure.com/api/projects/support-bot"
- Search by name: "Find the knowledge index named
product-faqs at endpoint https://my-example-resource.services.ai.azure.com/api/projects/my-project"
- Filter by tag: "List knowledge indexes tagged with
security at endpoint https://my-example-resource.services.ai.azure.com/api/projects/my-project"
- Show index details: "Show details for the
customer-service knowledge index at endpoint https://my-example-resource.services.ai.azure.com/api/projects/my-project"
| Parameter |
Required or optional |
Description |
| Endpoint |
Required |
The endpoint URL for the Microsoft Foundry project or service. The endpoint follows this pattern: https://<foundry-resource-name>.services.ai.azure.com/api/projects/<project-name>. |
Tool annotation hints:
| Destructive |
Idempotent |
Open World |
Read Only |
Secret |
Local Required |
| ❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
Knowledge: Get index schema
Get the detailed schema configuration of a specific knowledge index from Foundry.
This operation shows you comprehensive information about the structure and configuration of a knowledge index, including field definitions, data types, searchable attributes, and other schema properties. Use this schema information to understand how the index structures and indexes your data for searching.
Example CLI command
azmcp foundryextensions knowledge index schema \
--index <index> \
--endpoint <endpoint>
| Parameter |
Type |
Required |
Description |
index |
string |
Yes |
The name of the knowledge index. |
endpoint |
string |
Yes |
The endpoint URL for the Microsoft Foundry project or service. The endpoint follows this pattern: https://<foundry-resource-name>.services.ai.azure.com/api/projects/<project-name>. |
Example prompts include:
- View index schema: "Show me the schema for knowledge index
product-facts at endpoint https://my-example-resource.services.ai.azure.com/api/projects/my-project"
| Parameter |
Required or optional |
Description |
| Index |
Required |
The name of the knowledge index. |
| Endpoint |
Required |
The endpoint URL for the Microsoft Foundry project or service. The endpoint follows this pattern: https://<foundry-resource-name>.services.ai.azure.com/api/projects/<project-name>. |
Tool annotation hints:
| Destructive |
Idempotent |
Open World |
Read Only |
Secret |
Local Required |
| ❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
OpenAI: Create chat completions
Create chat completions by using Azure OpenAI in Foundry. Send messages to Azure OpenAI chat models deployed in your Foundry resource and receive AI-generated conversational responses. Supports multi-turn conversations with message history, system instructions, and response customization.
Example CLI command
azmcp foundryextensions openai chat-completions-create \
--resource-name <resource-name> \
--deployment <deployment> \
--message-array <message-array> \
--resource-group <resource-group> \
[--max-tokens <max-tokens>] \
[--temperature <temperature>] \
[--top-p <top-p>] \
[--frequency-penalty <frequency-penalty>] \
[--presence-penalty <presence-penalty>] \
[--stop <stop>] \
[--stream <stream>] \
[--seed <seed>] \
[--user <user>]
| Parameter |
Type |
Required |
Description |
resource-name |
string |
Yes |
The name of the Microsoft Foundry resource. |
deployment |
string |
Yes |
The name of the deployment. |
message-array |
string |
Yes |
JSON array of messages in the conversation. Each message should have role and content properties. |
resource-group |
string |
Yes |
The Azure resource group name. |
max-tokens |
string |
No |
The maximum number of tokens to generate in the completion. |
temperature |
string |
No |
Controls randomness in the output. Lower values make it more deterministic. |
top-p |
string |
No |
Controls diversity via nucleus sampling (0.0 to 1.0). Default is 1.0. |
frequency-penalty |
string |
No |
Penalizes new tokens based on their frequency (-2.0 to 2.0). Default is 0. |
presence-penalty |
string |
No |
Penalizes new tokens based on presence (-2.0 to 2.0). Default is 0. |
stop |
string |
No |
Up to 4 sequences where the API stops generating further tokens. |
stream |
string |
No |
Whether to stream back partial progress. Default is false. |
seed |
string |
No |
If specified, the system makes a best effort to sample deterministically. |
user |
string |
No |
User identifier for tracking and abuse monitoring. |
Example prompts include:
- Simple greeting: "Create a chat completion with message array
[{"role":"user","content":"Hello, how are you today?"}] using deployment gpt-35-turbo on resource openai-prod"
- With system message: "Create a chat completion with system message
You are a helpful assistant and user message Explain quantum computing using deployment gpt-35-turbo on resource openai-west"
- Control creativity: "Generate a chat completion for
Write a creative story using deployment gpt-4 with temperature 0.8 and max 150 tokens on resource ai-central"
- Deterministic response: "Create chat completion with message
List 5 facts about Mars using deployment gpt-35-turbo with temperature 0.1 and seed 12345 on resource ai-services-prod"
- Conversation with history: "Continue chat completion with messages: system
You are a coding assistant, user How do I create a function in Python?, assistant Here's how..., user Can you show an example? using deployment gpt-4 on resource dev-openai"
- With penalties for repetition: "Create completion for
Describe the benefits of cloud computing using deployment gpt-35-turbo with frequency penalty 0.5 and presence penalty 0.3 on resource ai-services-main"
- Streaming response: "Generate streaming chat completion for
Explain machine learning step by step using deployment gpt-4 with stream true on resource openai-research"
- With stop sequences: "Create completion for
Count from 1 to 10 using deployment gpt-35-turbo with stop sequences ['5', 'STOP'] on resource ai-test"
- User tracking: "Generate completion for
What is Azure AI? using deployment gpt-4 with user identifier user-123 on resource prod-openai"
- Fine-tuned control: "Create chat completion for
Summarize this article using deployment gpt-35-turbo with temperature 0.2, top_p 0.9, max tokens 200, and AAD authentication on resource secure-ai"
| Parameter |
Required or optional |
Description |
| Resource name |
Required |
The name of the Microsoft Foundry resource. |
| Deployment |
Required |
The name of the Foundry model deployment. |
| Message array |
Required |
JSON array of messages in the conversation. Each message should have role and content properties. |
| Resource group |
Required |
The Azure resource group name. |
| Max tokens |
Optional |
The maximum number of tokens to generate in the completion. |
| Temperature |
Optional |
Controls randomness in the output. Lower values make it more deterministic. |
| Top p |
Optional |
Controls diversity via nucleus sampling (0.0 to 1.0). Default is 1.0. |
| Frequency penalty |
Optional |
Penalizes new tokens based on their frequency (-2.0 to 2.0). Default is 0. |
| Presence penalty |
Optional |
Penalizes new tokens based on presence (-2.0 to 2.0). Default is 0. |
| Stop |
Optional |
Up to 4 sequences where the API will stop generating further tokens. |
| Stream |
Optional |
Whether to stream back partial progress. Default is false. |
| Seed |
Optional |
If specified, the system will make a best effort to sample deterministically. |
| User |
Optional |
User identifier for tracking and abuse monitoring. |
Tool annotation hints:
| Destructive |
Idempotent |
Open World |
Read Only |
Secret |
Local Required |
| ❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
OpenAI: Create embeddings
Create embeddings using Azure OpenAI in Foundry. Generate vector embeddings from text using Azure OpenAI deployments in your Foundry resource for semantic search, similarity comparisons, clustering, or machine learning.
Example CLI command
azmcp foundryextensions openai embeddings-create \
--resource-name <resource-name> \
--deployment <deployment> \
--input-text <input-text> \
--resource-group <resource-group> \
[--user <user>] \
[--encoding-format <encoding-format>] \
[--dimensions <dimensions>]
| Parameter |
Type |
Required |
Description |
resource-name |
string |
Yes |
The name of the Microsoft Foundry resource. |
deployment |
string |
Yes |
The name of the deployment. |
input-text |
string |
Yes |
The input text to generate embeddings for. |
resource-group |
string |
Yes |
The Azure resource group name. |
user |
string |
No |
User identifier for tracking and abuse monitoring. |
encoding-format |
string |
No |
The format to return embeddings in (float or base64). |
dimensions |
string |
No |
The number of dimensions for the embedding output. Only supported in some models. |
Example prompts include:
- Basic text embedding: "Generate embeddings for the text
Azure OpenAI Service by using my text-embedding-ada-002 deployment in resource group my-resource-group."
- Create vector embeddings: "Create vector embeddings for my text by using Azure OpenAI with deployment
text-embedding-3-large on resource ai-services-prod in resource group my-resource-group."
- Document embedding: "Generate embeddings for
Machine learning revolutionizes data analysis by using deployment ada-002 on resource embedding-service in resource group my-resource-group."
- Multiple sentences: "Create embeddings for the text
Cloud computing provides scalable infrastructure. It enables global accessibility. by using my embedding deployment in resource group my-resource-group."
- With user tracking: "Generate embeddings for
Natural language processing applications by using deployment text-embedding-3-small with user identifier analytics-team in resource group my-resource-group."
- Specific dimensions: "Create embeddings for
Artificial intelligence transforms business operations by using deployment text-embedding-3-large with 1536 dimensions on resource ai-central in resource group my-resource-group."
- Base64 format: "Generate embeddings for
Deep learning neural networks by using deployment ada-002 with base64 encoding format on resource ml-services in resource group my-resource-group."
- Research text: "Create vector embeddings for
Quantum computing demonstrates computational advantages in specific algorithms by using my text-embedding deployment in resource group my-resource-group."
- Product description: "Generate embeddings for
High-performance laptop with advanced graphics processing unit by using deployment text-embedding-3-small on resource product-ai in resource group my-resource-group."
- Technical documentation: "Create embeddings for
API authentication requires valid credentials and proper authorization headers by using deployment ada-002 with float encoding on resource docs-embedding in resource group my-resource-group."
| Parameter |
Required or optional |
Description |
| Resource name |
Required |
The name of the Microsoft Foundry resource. |
| Deployment |
Required |
The name of the Foundry model deployment. |
| Input text |
Required |
The input text to generate embeddings for. |
| Resource group |
Required |
The Azure resource group name. |
| User |
Optional |
User identifier for tracking and abuse monitoring. |
| Encoding format |
Optional |
The format to return embeddings in (float or base64). |
| Dimensions |
Optional |
The number of dimensions for the embedding output. Only supported in some models. |
Tool annotation hints:
| Destructive |
Idempotent |
Open World |
Read Only |
Secret |
Local Required |
| ❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
OpenAI: Create completions
Create text completions by using Azure OpenAI in Foundry. Send a prompt or question to Azure OpenAI models deployed in your Foundry resource and receive generated text answers. Use this feature when you need to create completions, get AI-generated content, generate answers to questions, or produce text completions from Azure OpenAI based on any input prompt. Supports customization with temperature and max tokens.
Example CLI command
azmcp foundryextensions openai create-completion \
--deployment <deployment> \
--prompt-text <prompt-text> \
--resource-name <resource-name> \
--resource-group <resource-group> \
[--max-tokens <max-tokens>] \
[--temperature <temperature>]
| Parameter |
Type |
Required |
Description |
deployment |
string |
Yes |
The name of the deployment. |
prompt-text |
string |
Yes |
The prompt text to send to the completion model. |
resource-name |
string |
Yes |
The name of the Microsoft Foundry resource. |
resource-group |
string |
Yes |
The Azure resource group name. |
max-tokens |
string |
No |
The maximum number of tokens to generate in the completion. |
temperature |
string |
No |
Controls randomness in the output. Lower values make it more deterministic. |
Example prompts include:
- Basic completion: "Create a completion with the prompt
What is Azure? using my gpt-35-turbo deployment in resource group my-resource-group"
- With temperature control: "Generate text completion for
Explain machine learning using deployment text-davinci-003 with temperature 0.3 in resource group my-resource-group"
- Limited tokens: "Create a completion with prompt
Write a summary using my gpt-4 deployment with max 100 tokens in resource group my-resource-group"
- Creative writing: "Generate completion for
Tell me a story about AI using deployment gpt-35-turbo with temperature 0.8 and 200 max tokens in resource group my-resource-group"
- Technical explanation: "Create completion with prompt
How does cloud computing work? using my OpenAI resource ai-services-east and deployment gpt-4 in resource group my-resource-group"
| Parameter |
Required or optional |
Description |
| Deployment |
Required |
The name of the deployment. |
| Prompt text |
Required |
The prompt text to send to the completion model. |
| Resource name |
Required |
The name of the Microsoft Foundry resource. |
| Resource group |
Required |
The Azure resource group name. |
| Max tokens |
Optional |
The maximum number of tokens to generate in the completion. |
| Temperature |
Optional |
Controls randomness in the output. Lower values make it more deterministic. |
Tool annotation hints:
| Destructive |
Idempotent |
Open World |
Read Only |
Secret |
Local Required |
| ❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
OpenAI: List models and deployments
List Azure OpenAI model deployments in a Microsoft Foundry resource. This tool retrieves information about
deployed models including model names, versions, capabilities, and deployment status.
Example CLI command
azmcp foundryextensions openai models-list \
--resource-name <resource-name> \
--resource-group <resource-group>
| Parameter |
Type |
Required |
Description |
resource-name |
string |
Yes |
The name of the Microsoft Foundry resource. |
resource-group |
string |
Yes |
The Azure resource group name. |
Example prompts include:
- View all models: "List all deployed OpenAI models in my
ai-services-prod resource in resource group my-resource-group."
- Check deployments: "Show me all deployed models and their status in resource
openai-east in resource group my-resource-group."
- Production inventory: "What models are deployed in my
production-openai resource in resource group my-resource-group?"
- Development check: "List all model deployments in my
dev-ai-services resource in resource group my-resource-group."
- Model capabilities: "Show me all deployed OpenAI models with their capabilities in resource
ai-central in resource group my-resource-group."
- Deployment status: "What's the current status of all deployments in my
openai-west resource in resource group my-resource-group?"
- Regional models: "List all models deployed in my
europe-openai resource in resource group my-resource-group."
- Service overview: "Give me a complete overview of model deployments in resource
customer-ai in resource group my-resource-group."
- Model versions: "Show me all deployed model versions in my
ai-services-main resource in resource group my-resource-group."
- Resource audit: "I need to audit all OpenAI model deployments in resource
enterprise-ai in resource group my-resource-group."
| Parameter |
Required or optional |
Description |
| Resource name |
Required |
The name of the Microsoft Foundry resource. |
| Resource group |
Required |
The Azure resource group name. |
Tool annotation hints:
| Destructive |
Idempotent |
Open World |
Read Only |
Secret |
Local Required |
| ❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
Resources: Get Foundry resource
List or get Microsoft Foundry resources and return resource-level details such as endpoint URL, location, SKU, kind, and provisioning state. If you provide a resource name, the tool returns that single resource. Otherwise, it returns the resource inventory in scope. For model deployment inventory inside a Foundry resource, use the OpenAI models list tool.
Example CLI command
azmcp foundryextensions resource get \
[--resource-name <resource-name>]
| Parameter |
Type |
Required |
Description |
resource-name |
string |
No |
The name of the Microsoft Foundry resource. |
Example prompts include:
- Get specific resource: "Show me details for the
ai-foundry-prod Foundry resource"
- List all resources: "What Foundry resources do I have in my subscription?"
- Resource with configuration: "Get the endpoint URL, location, and SKU information for my
customer-ai-foundry foundry resource"
| Parameter |
Required or optional |
Description |
| Resource name |
Optional |
The name of the Microsoft Foundry resource. |
Tool annotation hints:
| Destructive |
Idempotent |
Open World |
Read Only |
Secret |
Local Required |
| ❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
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