Support index descriptions end-to-end

.env.example

@@ -18,7 +18,7 @@ AZURE_OPENAI_API_VERSION=
 
 # Additional info for Knowledge
 AZURE_AI_SEARCH_ENDPOINT=https://<your-search-service-name>.search.windows.net/
-AZURE_AI_SEARCH_API_VERSION=2025-03-01-preview
+AZURE_AI_SEARCH_API_VERSION=2025-05-01-preview
 SEARCH_AUTHENTICATION_METHOD=api-search-key
 AZURE_AI_SEARCH_API_KEY=your_api_key
 

README.md

@@ -29,6 +29,7 @@ A Model Context Protocol server for Azure AI Foundry, providing a unified set of
 | Category | Tool | Description |
 |---|---|---|
 | **Index** | `list_index_names` | Retrieve all names of indexes from the AI Search Service |
+|  | `list_indexes_with_descriptions` | Retrieve the names and descriptions of all 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 |
@@ -172,7 +173,7 @@ See [example .env file](./clients/python/pydantic-ai/.env.example) for a sample
 | -------------- | ----------------------------- | ---------------------------------- | ------------------------------------------------ |
 | **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`. |
+|                | `AZURE_AI_SEARCH_API_VERSION` | No                                 | API Version to use. Defaults to `2025-05-01-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) |

pyproject.toml

@@ -10,7 +10,8 @@ dependencies = [
     "azure-mgmt-cognitiveservices>=13.0.0",
     "azure-identity>=1.0",
     "jinja2~=3.0",
-    "azure-search-documents>=11.5.2",
+    "azure-search-documents>=11.7.0b1",
+    "python-dotenv>=1.0.1",
     "azure-cli>=2.60.0",
     "azure-ai-evaluation>=1.9.0",
     "azure-ai-projects>=1.0.0b11"

src/mcp_foundry/mcp_foundry_knowledge/data_access_objects/dao.py

@@ -123,6 +123,21 @@ def retrieve_index_names(self) -> list[str]:
 
         return results
 
+    def retrieve_indexes_with_descriptions(self) -> list[MutableMapping[str, Any]]:
+        """
+        Retrieves the names and descriptions of all search indexes.
+
+        Returns:
+            list[MutableMapping[str, Any]]: A list of dictionaries, each containing 'name' and 'description' 
+            keys for each search index. Description may be None if not set.
+        """
+        search_results: ItemPaged[SearchIndex] = self.client.list_indexes()
+
+        return [
+            {'name': index.name, 'description': index.description}
+            for index in search_results
+        ]
+
     def retrieve_index_schemas(self) -> list[MutableMapping[str, Any]]:
         """
         Retrieves the full schema definition for each search index.

src/mcp_foundry/mcp_foundry_knowledge/data_access_objects/models.py

@@ -44,6 +44,7 @@ class ScoringProfileSchema(BaseModel):
 
 class SearchIndexSchema(BaseModel):
     name: str
+    description: Optional[str] = None
     fields: List[SearchFieldSchema]
     suggesters: Optional[List[SuggesterSchema]] = None
     scoring_profiles: Optional[List[ScoringProfileSchema]] = None
@@ -64,6 +65,7 @@ def convert_pydantic_model_to_search_index(schema: SearchIndexSchema) -> SearchI
 
     return SearchIndex(
         name=schema.name,
+        description=schema.description,
         fields=fields,
         suggesters=suggesters or None,
         scoring_profiles=schema.scoring_profiles,
@@ -92,4 +94,3 @@ def convert_to_field_mappings(models: List[FieldMappingModel]) -> List[FieldMapp
         )
         for model in models
     ]
-

src/mcp_foundry/mcp_foundry_knowledge/tools.py

@@ -68,6 +68,18 @@ async def list_index_names() -> list[str]:
     dao = SearchIndexDao()
     return dao.retrieve_index_names()
 
+@mcp.tool(description="Retrieves the names and descriptions of all indexes")
+async def list_indexes_with_descriptions() -> list[dict[str, str | None]]:
+    """
+    Retrieves the names and descriptions of all indexes.
+
+    Returns:
+        list[dict[str, str | None]]: A list of dictionaries, each containing 'name' and 'description' 
+        keys for each search index. Description may be None if not set.
+    """
+    dao = SearchIndexDao()
+    return dao.retrieve_indexes_with_descriptions()
+
 @mcp.tool(description="Retrieves the schemas for all indexes ")
 async def list_index_schemas() -> list[OperationResult]:
     """

tests/test_knowledge_dao.py

@@ -0,0 +1,52 @@
+import pytest
+
+from mcp_foundry.mcp_foundry_knowledge.data_access_objects.dao import SearchIndexDao
+from mcp_foundry.mcp_foundry_knowledge.data_access_objects.models import (
+    SearchIndexSchema,
+    SearchFieldSchema,
+    convert_pydantic_model_to_search_index,
+)
+
+
+def test_retrieve_indexes_with_descriptions(monkeypatch):
+    class FakeIndex:
+        def __init__(self, name: str, description: str | None):
+            self.name = name
+            self.description = description
+
+    class FakeClient:
+        def list_indexes(self):
+            return [
+                FakeIndex("idx1", "Description 1"),
+                FakeIndex("idx2", None),
+            ]
+
+    def fake_init(self):
+        self.client = FakeClient()
+
+    monkeypatch.setattr(SearchIndexDao, "__init__", fake_init, raising=False)
+
+    dao = SearchIndexDao()
+    results = dao.retrieve_indexes_with_descriptions()
+
+    assert results == [
+        {"name": "idx1", "description": "Description 1"},
+        {"name": "idx2", "description": None},
+    ]
+
+
+def test_convert_pydantic_model_to_search_index_description_passthrough():
+    schema = SearchIndexSchema(
+        name="test-index",
+        description="My test index",
+        fields=[
+            SearchFieldSchema(name="id", type="Edm.String", key=True),
+        ],
+    )
+
+    search_index = convert_pydantic_model_to_search_index(schema)
+
+    assert getattr(search_index, "description", None) == "My test index"
+    assert search_index.name == "test-index"
+    assert hasattr(search_index, "fields") and len(search_index.fields) == 1
+