Overhaul MCP docs for Custom DC bundled server

custom_dc/advanced.md

@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Advanced (hybrid) setups
-nav_order: 10
+nav_order: 11
 parent: Build your own Data Commons
 ---
 

custom_dc/custom_data.md

@@ -466,7 +466,7 @@ If the servers have started up without errors, check to ensure that your data is
 
 1. Verify statistical variables: go to the [Statistical Variable Explorer](https://localhost:8080/tools/statvar){: target="_blank"} to verify that your statistical variables are showing up correctly. You should see something like this:
 
-    ![](/assets/images/custom_dc/customdc_screenshot11.png){: width="400"}
+  ![](/assets/images/custom_dc/customdc_screenshot11.png){: width="400"}
 1. Click on a variable name to get more information on the right panel.
 1. Verify that your observations are loaded: Click on an **Example Place** link to open the detailed page for that place. Scroll to the bottom, where you should see a timeline graph of observations for the selected place.
 1. Verify natural-language querying: go to the [Search page](https://localhost:8080/tools/explore){: target="_blank"} and enter a query related to your data. You should get relevant graphs using your data.

custom_dc/deploy_cloud.md

@@ -1,14 +1,14 @@
 ---
 layout: default
 title: Deploy to Google Cloud
-nav_order: 8
+nav_order: 9
 parent: Build your own Data Commons
 ---
 
 {: .no_toc}
 # Deploy your custom instance to Google Cloud
 
-This page shows you how to create a development environment in Google Cloud Platform, using [Terraform](https://cloud.google.com/docs/terraform){: target="_blank"}. This is step 4 of the [recommended workflow](/custom_dc/index.html#workflow).
+This page shows you how to create a development environment in Google Cloud Platform, using [Terraform](https://cloud.google.com/docs/terraform){: target="_blank"}. This is step 5 of the [recommended workflow](/custom_dc/index.html#workflow).
 
 > **Note**: It's recommended that you go through the [Quickstart](quickstart.md) to start up a local instance before attempting to set up a Google Cloud instance. This will ensure you have all the necessary prerequisites, and give you a chance to test out your own data to make sure everything is working.
 
@@ -114,6 +114,7 @@ All of the deployment options you can configure are listed in [deploy/terraform-
 | `dc_web_service_image` | `gcr.io/datcom-ci/datacommons-services:stable` | Specifies the image for the Docker services container. You will want to change this to a custom image once you have created it in [Upload a custom Docker image](#upload). |
 | `make_dc_web_service_public` | `true` | If you intend to restrict access to your instance, set this to `false`. |
 | `disable_google_maps` | `false` | If you want to disable showing Google Maps in the website, set this to `true`. |
+| `dc_search_scope` | `base_and_custom` | If you want to limit AI agent queries to only searching your custom data, set this to `custom_only`. |
 
 Other recommended settings for a production environment are provided in [Launch your Data Commons](launch_cloud.md#create-env).
 
@@ -372,6 +373,14 @@ The URL for your service is in the form <code>https://<var>NAMESPACE</var>-datac
 
 If the link is not clickable and the service is not running, go back to the Console Cloud Run page, click the  **Logs** tab and look for errors. Also check the output of your `terraform apply` run.
 
+### Connect an AI agent to the MCP server
+
+To connect an AI agent to the cloud service:
+
+1. Obtain the app URL from the previous step.
+1. In the configuration for the agent/client, specify the HTTP URL as <code>https://<var>APP_URL</var>/mcp</code>. 
+1. Run the agent as usual.
+
 <script src="/assets/js/customdc-doc-tabs.js"></script>
 
 ## Update your Terraform deployment {#update-terraform}

custom_dc/faq.md

@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Frequently asked questions
-nav_order: 12
+nav_order: 13
 parent: Build your own Data Commons
 ---
 

custom_dc/index.md

@@ -40,6 +40,7 @@ For the following use cases, a custom Data Commons instance is not necessary:
 |--------------------------------------------------------------|--------------------|---------------------|
 | Interactive tools (Exploration tools, Statistical Variable Explorer, etc.) |  yes  |    yes    |
 | Natural language query interface                            | yes, using Google AI technologies and models  |  yes, using open-source models only<sup>1</sup>  |
+| Model Context Protocol (MCP) server | yes | yes |
 | REST APIs                                                   |  yes | yes |
 | Python and Pandas API wrappers                               |  yes  | yes |
 | Google Spreadsheets                                         |  yes |  no<sup>2</sup> |
@@ -60,7 +61,6 @@ Essentially, a custom Data Commons instance is a mirror of the public Data Commo
 
 A custom Data Commons instance uses custom data that you provide as raw CSV files. An importer script converts the CSV data into the Data Commons format and stores this in a SQL database. For local development, we provide a lightweight, open-source [SQLite](http://sqlite.org) database; for production, we recommend that you use [Google Cloud SQL](https://cloud.google.com/sql/){: target="_blank"}.
 
-
 > **Note**: You have full control and ownership of your data, which will live in SQL data stores that you own and manage. Your data is never transferred to the base Data Commons data stores managed by Google; see full details in this [FAQ](/custom_dc/faq.html#data-security). 
 
 In addition to the data, a custom Data Commons instance consists of two Docker containers: 
@@ -71,7 +71,7 @@ Details about the components that make up the containers are provided in the [Qu
 
 ## Requirements and cost
 
-A custom Data Commons site runs in a Docker container on Google Cloud Platform (GCP), using Google Cloud Run, a serverless solution that provides auto-scaling and other benefits. You will need the following:
+A custom Data Commons site runs in Docker containers on Google Cloud Platform (GCP), using Google Cloud Run, a serverless solution that provides auto-scaling and other benefits. You will need the following:
 
 - A [GCP](http://console.cloud.google.com) billing account and project
 - A [Docker](http://docker.com) account
@@ -98,6 +98,7 @@ You may also need Cloud DNS, Networking - Cloud Loadbalancing, and Redis Memorys
 1. Prepare your real-world data and load it in the local custom instance. Data Commons requires your data to be in a specific format. See [Prepare and load your own data](/custom_dc/custom_data.html) for details. 
 > Note: This section is very important!  If your data is not in the scheme Data Commons expects, it won't load.
 1. If you want to customize the look of the feel of the site, see [Customize the site](/custom_dc/custom_ui.html) and [Build a custom image](build_images.md).
+1. Optionally, configure an AI agent to send NL queries to the MCP server (via an LLM). See [Run MCP tools](run_mcp_tools.md).
 1. When you have finished testing locally, set up a development environment in Google Cloud Platform. See [Deploy to Google Cloud](/custom_dc/deploy_cloud.html).
 1. Productionize and launch your site for external traffic. See [Launch your Data Commons](/custom_dc/launch_cloud.html).
 1. For future updates and launches, continue to make UI and data changes locally, before deploying the changes to GCP.

custom_dc/launch_cloud.md

@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Launch your Data Commons
-nav_order: 9
+nav_order: 10
 parent: Build your own Data Commons
 ---
 

custom_dc/quickstart.md

@@ -29,7 +29,8 @@ The "services" Docker container consists of the following Data Commons component
 - A [Nginx reverse proxy server](https://www.nginx.com/resources/glossary/reverse-proxy-server/){: target="_blank"}, which routes incoming requests to the web or API server
 - A Python-Flask web server, which handles interactive requests from users
 - An Python-Flask NL server, for serving natural language queries
-- A Go Mixer, also known as the API server, which serves programmatic requests using Data Commons APIs. The SQL query engine is built into the Mixer, which sends queries to both the local and remote data stores to find the right data. If the Mixer determines that it cannot fully resolve a user query from the custom data, it will make an REST API call, as an anonymous "user" to the base Data Commons Mixer and data.
+- An [MCP server](https://modelcontextprotocol.io/){: target="_blank"}, for serving tool responses to an MCP-compliant AI agent (e.g. Google ADK apps, Gemini CLI, Google Antigravity)
+- A Go Mixer, also known as the API server, which serves programmatic requests using Data Commons APIs. The SQL query engine is built into the Mixer, which sends queries to both the local and remote data stores to find the right data. If the Mixer determines that it cannot fully resolve a user query from the custom data, it will make a REST API call, as an anonymous "user" to the base Data Commons Mixer and data.
 
 ## Prerequisites
 
@@ -167,7 +168,7 @@ This does the following:
 - Imports the data from the CSV files, resolves entities, and writes the data to a SQLite database file, `custom_dc/sample/datacommons/datacommons.db`.
 - Generates embeddings in `custom_dc/sample/datacommons/nl`. (To learn more about embeddings generation, see the [FAQ](/custom_dc/faq.html#natural-language-processing)).
 - Starts the services Docker container.
-- Starts development/debug versions of the Web Server, NL Server, and Mixer, as well as the Nginx proxy, inside the container.
+- Starts development/debug versions of the Web server, MCP server, NL server, and Mixer, as well as the Nginx proxy, inside the container.
 - Maps the output sample data to a Docker path.
 
 You can see the actual Docker commands that the script runs at the [end of this page](#docker).