chore: conditional usage of vector search param

Author: gagik

src/tools/mongodb/create/insertMany.ts

@@ -25,22 +25,30 @@ export class InsertManyTool extends MongoDBToolBase {
             .describe(
                 "The array of documents to insert, matching the syntax of the document argument of db.collection.insertMany()."
             ),
-        embeddingParameters: zSupportedEmbeddingParametersWithInput
-            .optional()
-            .describe(
-                "The embedding model and its parameters to use to generate embeddings for fields with vector search indexes. Note to LLM: If unsure which embedding model to use, ask the user before providing one."
-            ),
+        ...(this.isFeatureEnabled("vectorSearch")
+            ? {
+                  embeddingParameters: zSupportedEmbeddingParametersWithInput
+                      .optional()
+                      .describe(
+                          "The embedding model and its parameters to use to generate embeddings for fields with vector search indexes. Note to LLM: If unsure which embedding model to use, ask the user before providing one."
+                      ),
+              }
+            : {}),
     };
     public operationType: OperationType = "create";
 
     protected async execute({
         database,
         collection,
         documents,
-        embeddingParameters,
+        embeddingParameters: providedEmbeddingParameters,
     }: ToolArgs<typeof this.argsShape>): Promise<CallToolResult> {
         const provider = await this.ensureConnected();
 
+        const embeddingParameters = this.isFeatureEnabled("vectorSearch")
+            ? (providedEmbeddingParameters as z.infer<typeof zSupportedEmbeddingParametersWithInput>)
+            : undefined;
+
         // Process documents to replace raw string values with generated embeddings
         documents = await this.replaceRawValuesWithEmbeddingsIfNecessary({
             database,

tests/accuracy/insertMany.embeddings.test.ts

@@ -187,7 +187,7 @@ describeAccuracyTests(
         },
     ],
     {
-        userConfig: { voyageApiKey: "valid-key" },
+        userConfig: { voyageApiKey: "valid-key", previewFeatures: "vectorSearch" },
         clusterConfig: {
             search: true,
         },

tests/integration/tools/mongodb/create/insertMany.test.ts

@@ -28,13 +28,6 @@ describeWithMongoDB("insertMany tool when search is disabled", (integration) =>
                 "The array of documents to insert, matching the syntax of the document argument of db.collection.insertMany().",
             required: true,
         },
-        {
-            name: "embeddingParameters",
-            type: "object",
-            description:
-                "The embedding model and its parameters to use to generate embeddings for fields with vector search indexes. Note to LLM: If unsure which embedding model to use, ask the user before providing one.",
-            required: false,
-        },
     ]);
 
     validateThrowsForInvalidArguments(integration, "insert-many", [
@@ -598,11 +591,42 @@ describeWithMongoDB(
         getUserConfig: () => ({
             ...defaultTestConfig,
             voyageApiKey: process.env.TEST_MDB_MCP_VOYAGE_API_KEY ?? "",
+            previewFeatures: ["vectorSearch"],
         }),
         downloadOptions: { search: true },
     }
 );
 
+describeWithMongoDB(
+    "insertMany tool when vector search is enabled",
+    (integration) => {
+        validateToolMetadata(integration, "insert-many", "Insert an array of documents into a MongoDB collection", [
+            ...databaseCollectionParameters,
+            {
+                name: "documents",
+                type: "array",
+                description:
+                    "The array of documents to insert, matching the syntax of the document argument of db.collection.insertMany().",
+                required: true,
+            },
+            {
+                name: "embeddingParameters",
+                type: "object",
+                description:
+                    "The embedding model and its parameters to use to generate embeddings for fields with vector search indexes. Note to LLM: If unsure which embedding model to use, ask the user before providing one.",
+                required: false,
+            },
+        ]);
+    },
+    {
+        getUserConfig: () => ({
+            ...defaultTestConfig,
+            voyageApiKey: "valid-key",
+            previewFeatures: ["vectorSearch"],
+        }),
+    }
+);
+
 function extractInsertedIds(content: string): ObjectId[] {
     expect(content).toContain("Documents were inserted successfully.");
     expect(content).toContain("Inserted IDs:");