# Vertex AI Vector Search MCP for AI Agents AI Agent Connect > Vertex AI Vector Search MCP. Give your AI agent the ability to query billions of semantic embeddings and manage Google Cloud Vector Index endpoints directly. Skip the Cloud Console and handle nearest neighbor lookups, index status checks, and operation tracking in your chat interface. ## Overview - **Category:** ai-frontier - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_7qmxXVVjzgLzy5Qveg1Au3aDsjeacjAMcHp2XBIt/ai-agent-connect - **Tags:** vector-search, embeddings, nearest-neighbor, semantic-matching, cloud-infrastructure, low-latency ## Description Vertex AI Vector Search MCP lets you pull the matching scale of Google Cloud's vector search directly into your agent. You can perform low-latency nearest neighbor lookups across massive embedding structures without jumping through the Cloud Console. It lets you provide your agent with accurate data by retrieving matching text segments instantly. You can also see exactly what's happening with your datasets by asking the agent to list provisioned indexes, verify dimensionality, and check active states. It handles endpoint monitoring for your RAG applications so you know which iterations are receiving production traffic. Even for multi-terabyte builds, you can query the cloud queue to review long-running task timelines from your editor. When you're managing complex ML pipelines, having this level of visibility via a chat interface makes life a lot easier. You can find this Connector in the Vinkius catalog to get started. It's built for people who need to move fast between coding and cloud management, giving you a way to interact with your infrastructure as if it were just another part of your conversation. It removes the need for manual configuration and lets you focus on building better search experiences for your users. By connecting this tool, you turn a complex cloud operation into a simple set of conversational commands. ## Tools ### list_deployed_indexes See every index currently deployed to a specific endpoint. It's the fastest way to check what's live. ### list_index_endpoints View all index endpoints in your project. Use this to map out your production infrastructure. ### list_vector_indexes List every vector index in your project. This gives you a full bird's eye view of your data assets. ### list_vector_operations Check the status of long-running tasks. It's perfect for tracking multi-terabyte index builds. ### search_nearest_neighbors Perform a similarity search using a query vector and an endpoint ID. This is the core tool for RAG applications. ### get_index_details Get the configuration and metadata for a specific vector index. This helps you verify dimensions and settings quickly. ## Prompt Examples **Prompt:** ``` Show me all the vector indexes we have in our production project. ``` **Response:** ``` I've scanned your Vertex AI environment. You have 3 active vector indexes: | Index Name | ID | Dimensions | Status | | :--- | :--- | :--- | :--- | | `product_catalog` | `idx_992...` | 768 | **Active** | | `user_embeddings` | `idx_104...` | 1536 | **Active** | | `doc_vault_prod` | `idx_552...` | 1024 | **Active** | Would you like me to check the deployment status for any of these? ``` **Prompt:** ``` Is the big document vault index finished building yet? ``` **Response:** ``` I've checked the long-running operations for your project. **Operation Status:** - **Target:** `doc_vault_prod` index build - **Progress:** 87% complete - **Status:** `RUNNING` - **Estimated Time:** ~12 minutes remaining Everything looks healthy. I can check this again for you in a few minutes if you'd like. ``` **Prompt:** ``` Find the 3 closest items for this query vector: [0.1, -0.2, 0.5]. ``` **Response:** ``` I've performed a nearest neighbor search using your production endpoint. Here are the top 3 matches: 1. **ID:** `prod_8821` (Distance: 0.12) 2. **ID:** `prod_4490` (Distance: 0.19) 3. **ID:** `prod_1102` (Distance: 0.24) These results show a very high degree of semantic similarity to your query. ``` ## Capabilities ### Query nearest neighbors Get the most similar items from your vector database using a query vector. ### List active endpoints See every network endpoint currently live and receiving production traffic. ### Check index status View the configuration and active state of your provisioned vector indexes. ### Track long-running tasks Review the progress and timelines of multi-terabyte index builds. ### Verify index details Grab specific metadata and dimensionality for any index in your project. ## Use Cases ### Monitoring long-running index builds An MLOps engineer is worried a 5TB index build failed. They ask the agent to check the operations log. The agent uses list_vector_operations to show it's at 80% and running fine. ### Testing RAG search precision A data scientist wants to see if a new embedding model is working. They ask the agent to find the 3 nearest neighbors for a test query. The agent uses search_nearest_neighbors to return the IDs. ### Auditing cloud infrastructure A backend dev needs to audit the cloud setup. They ask the agent to list all endpoints. The agent uses list_index_endpoints to provide a full list of production URLs. ### Verifying production index status A developer needs to know if a specific index is ready. They ask the agent to check the status of the product_catalog index. The agent uses get_index_details to confirm it's active. ## Benefits - Stop switching tabs by using list_index_endpoints to see your entire production setup in one view. - Get instant feedback on RAG accuracy by using search_nearest_neighbors to test vector proximity. - Save time on MLOps by using list_vector_operations to monitor long-running index builds without refreshing pages. - Verify your data structure immediately with get_index_details to ensure dimensions match your requirements. - Reduce deployment errors by using list_deployed_indexes to confirm which versions are actually live. ## How It Works The bottom line is you get direct, conversational control over Google's vector search infrastructure without leaving your workspace. 1. Enable the Vertex AI API in your Google Cloud project. 2. Provide your Project ID, Location, and OAuth2 credentials to the Connector. 3. Ask your agent to search vectors or check your index status. ## Frequently Asked Questions **Can I use the Vertex AI Vector Search MCP to see my index status?** Yes, you can ask your agent to check the active state and configuration of any index in your project. It will return the dimensionality, configuration details, and current status instantly. **How does this Connector help with RAG applications?** It allows your agent to perform semantic searches by pulling the nearest neighbors for any query vector you provide. This helps ground your agent's responses in your own data. **Can I check if my vector deployments are actually live?** Yes, you can ask the agent to list all deployed indexes for a specific endpoint. This confirms which specific versions are currently live and receiving production traffic. **What happens if a multi-terabyte index build takes a long time?** You can use the Connector to query the operation logs and see the exact progress percentage and timeline. This lets you monitor large-scale builds without refreshing the cloud console. **Does this Connector work with other cloud providers?** No, this Connector is specifically built for Google Cloud's Vertex AI Vector Search service. It connects directly to Google Cloud infrastructure. **Can I see the dimensions of my embeddings?** Yes, the Connector can pull the metadata for any index to show you the dimensionality and other core settings. This is useful for ensuring your embedding models match your index configurations. **How do I perform a nearest-neighbor similarity test via chat?** Just write: `Search my endpoint '1xxx' against index 'deployed_abc_1' looking for 3 nearest neighbors to the vector [0.015, -0.042, 0.111]`. The `queryIndexTool` bridges to Vertex and returns the IDs and distances of your geometrical matches instantly. **Can I query a status for indices that take hours to build on GCP?** Absolutely. Use the prompt: `Check my google cloud vector operations.` The `listOperationsTool` reveals all in-flight Cloud operations indicating completion percentages and precise timestamps, allowing you to sidestep the Google Console completely. **Where do I easily find the short-lived VERTEX_ACCESS_TOKEN?** On your terminal with `gcloud` installed and logged in, simply type `gcloud auth print-access-token`. Copy the output stream starting with `ya29...` into your configurations and the integration is ready for connection.