deploy: feat(embeddingModel): Add Backend API selection fields (googleapis#2592)

Allow users to use both VertexAI and Google AI as gemini embedding model
backend.
- Use ADC for VertexAI authentication (either configure `project` and
`location` inside the yaml file or as env vars).
- Use API key for Google AI authentication. 912aa9e

Author: pull[bot]

dev/documentation/configuration/embedding-models/gemini/index.html

@@ -2,16 +2,14 @@
 <p>Google Gemini provides state-of-the-art embedding models that convert text into
 high-dimensional vectors.</p>
 <h3 id="authentication">Authentication</h3>
-<p>Toolbox uses your <a href="https://cloud.google.com/docs/authentication#adc">Application Default Credentials
-(ADC)</a> to authorize with the
-Gemini API client.</p>
-<p>Optionally, you can use an <a href="https://ai.google.dev/gemini-api/docs/api-key#api-keys">API key</a> obtain an API
-Key from the <a href="https://aistudio.google.com/app/apikey">Google AI Studio</a>.</p>
-<p>We recommend using an API key for testing and using application default
-credentials for production.</p>
-<h2 id="behavior">Behavior</h2>
-<h3 id="automatic-vectorization">Automatic Vectorization</h3>
-<p>When a tool parameter is configured with <code>embeddedBy: <your-gemini-model-name></code>,
-the Toolbox intercepts the raw text input from the client and sends it to the
-Gemini API. The resulting numerical array is then formatted before being passed
-to your database source.&lt;/p></description></item></channel></rss>
+&lt;p>Toolbox supports two authentication modes:&lt;/p>
+&lt;ol>
+&lt;li>&lt;strong>Google AI (API Key):&lt;/strong> Used if you
+provide &lt;code>apiKey&lt;/code> (or set &lt;code>GOOGLE_API_KEY&lt;/code>/&lt;code>GEMINI_API_KEY&lt;/code> environment
+variables). This uses the &lt;a href="https://aistudio.google.com/app/apikey">Google AI Studio&lt;/a> backend.&lt;/li>
+&lt;li>&lt;strong>Vertex AI (ADC):&lt;/strong> Used if provided &lt;code>project&lt;/code> and &lt;code>location&lt;/code> (or set
+&lt;code>GOOGLE_CLOUD_PROJECT&lt;/code>/&lt;code>GOOGLE_CLOUD_LOCATION&lt;/code> environment variables). This uses &lt;a href="https://cloud.google.com/docs/authentication#adc">Application
+Default Credentials (ADC)&lt;/a>.&lt;/li>
+&lt;/ol>
+&lt;p>We recommend using an API key for quick testing and using Vertex AI with ADC for
+production environments.&lt;/p></description></item></channel></rss>

dev/documentation/configuration/embedding-models/index.html

@@ -4140,19 +4140,17 @@ reporting with source positions and code snippets.</p>
 <p>Google Gemini provides state-of-the-art embedding models that convert text into
 high-dimensional vectors.</p>
 <h3 id="authentication">Authentication</h3>
-<p>Toolbox uses your <a href="https://cloud.google.com/docs/authentication#adc">Application Default Credentials
-(ADC)</a> to authorize with the
-Gemini API client.</p>
-<p>Optionally, you can use an <a href="https://ai.google.dev/gemini-api/docs/api-key#api-keys">API key</a> obtain an API
-Key from the <a href="https://aistudio.google.com/app/apikey">Google AI Studio</a>.</p>
-<p>We recommend using an API key for testing and using application default
-credentials for production.</p>
-<h2 id="behavior">Behavior</h2>
-<h3 id="automatic-vectorization">Automatic Vectorization</h3>
-<p>When a tool parameter is configured with <code>embeddedBy: <your-gemini-model-name></code>,
-the Toolbox intercepts the raw text input from the client and sends it to the
-Gemini API. The resulting numerical array is then formatted before being passed
-to your database source.&lt;/p></description></item><item><title>Google Sign-In</title><link>https://mcp-toolbox.dev/dev/documentation/configuration/authentication/google/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://mcp-toolbox.dev/dev/documentation/configuration/authentication/google/</guid><description>&lt;h2 id="getting-started">Getting Started&lt;/h2>
+&lt;p>Toolbox supports two authentication modes:&lt;/p>
+&lt;ol>
+&lt;li>&lt;strong>Google AI (API Key):&lt;/strong> Used if you
+provide &lt;code>apiKey&lt;/code> (or set &lt;code>GOOGLE_API_KEY&lt;/code>/&lt;code>GEMINI_API_KEY&lt;/code> environment
+variables). This uses the &lt;a href="https://aistudio.google.com/app/apikey">Google AI Studio&lt;/a> backend.&lt;/li>
+&lt;li>&lt;strong>Vertex AI (ADC):&lt;/strong> Used if provided &lt;code>project&lt;/code> and &lt;code>location&lt;/code> (or set
+&lt;code>GOOGLE_CLOUD_PROJECT&lt;/code>/&lt;code>GOOGLE_CLOUD_LOCATION&lt;/code> environment variables). This uses &lt;a href="https://cloud.google.com/docs/authentication#adc">Application
+Default Credentials (ADC)&lt;/a>.&lt;/li>
+&lt;/ol>
+&lt;p>We recommend using an API key for quick testing and using Vertex AI with ADC for
+production environments.&lt;/p></description></item><item><title>Google Sign-In</title><link>https://mcp-toolbox.dev/dev/documentation/configuration/authentication/google/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://mcp-toolbox.dev/dev/documentation/configuration/authentication/google/</guid><description>&lt;h2 id="getting-started">Getting Started&lt;/h2>
 &lt;p>Google Sign-In manages the OAuth 2.0 flow and token lifecycle. To integrate the
 Google Sign-In workflow to your web app &lt;a href="https://developers.google.com/identity/sign-in/web/sign-in">follow this guide&lt;/a>.&lt;/p>
 &lt;p>After setting up the Google Sign-In workflow, you should have registered your
@@ -7417,7 +7415,7 @@ returned. Otherwise, the selection is not guaranteed.&lt;/p>
 &lt;p>The tool returns a single JSON object representing the document, wrapped in a
 JSON array.&lt;/p>
 &lt;h2 id="compatible-sources">Compatible Sources&lt;/h2>
-&lt;h2 id="hahahugoshortcode339s0hbhb">
+&lt;h2 id="hahahugoshortcode340s0hbhb">
 
 
 &lt;div class="compatibility-section">

dev/documentation/configuration/embedding-models/index.xml

@@ -94,7 +94,7 @@ returned. Otherwise, the selection is not guaranteed.</p>
 <p>The tool returns a single JSON object representing the document, wrapped in a
 JSON array.</p>
 <h2 id="compatible-sources">Compatible Sources</h2>
-<h2 id="hahahugoshortcode339s0hbhb">
+<h2 id="hahahugoshortcode340s0hbhb">
 
 
 <div class="compatibility-section">

dev/index.xml

@@ -506,11 +506,11 @@
 <a href=https://github.com/googleapis/genai-toolbox/edit/main/docs/en/integrations/mongodb/tools/mongodb-find-one.md class="td-page-meta--edit td-page-meta__edit" target=_blank rel=noopener><i class="fa-solid fa-pen-to-square fa-fw"></i> Edit this page</a>
 <a href="https://github.com/googleapis/genai-toolbox/new/main/docs/en/integrations/mongodb/tools?filename=change-me.md&amp;value=---%0Atitle%3A+%22Long+Page+Title%22%0AlinkTitle%3A+%22Short+Nav+Title%22%0Aweight%3A+100%0Adescription%3A+%3E-%0A+++++Page+description+for+heading+and+indexes.%0A---%0A%0A%23%23+Heading%0A%0AEdit+this+template+to+create+your+new+page.%0A%0A%2A+Give+it+a+good+name%2C+ending+in+%60.md%60+-+e.g.+%60getting-started.md%60%0A%2A+Edit+the+%22front+matter%22+section+at+the+top+of+the+page+%28weight+controls+how+its+ordered+amongst+other+pages+in+the+same+directory%3B+lowest+number+first%29.%0A%2A+Add+a+good+commit+message+at+the+bottom+of+the+page+%28%3C80+characters%3B+use+the+extended+description+field+for+more+detail%29.%0A%2A+Create+a+new+branch+so+you+can+preview+your+new+file+and+request+a+review+via+Pull+Request.%0A" class="td-page-meta--child td-page-meta__child" target=_blank rel=noopener><i class="fa-solid fa-pen-to-square fa-fw"></i> Create child page</a>
 <a href="https://github.com/googleapis/genai-toolbox/issues/new?title=mongodb-find-one" class="td-page-meta--issue td-page-meta__issue" target=_blank rel=noopener><i class="fa-solid fa-list-check fa-fw"></i> Create documentation issue</a>
-<a href=https://github.com/googleapis/genai-toolbox/issues/new class="td-page-meta--project td-page-meta__project-issue" target=_blank rel=noopener><i class="fa-solid fa-list-check fa-fw"></i> Create project issue</a></div><div class=td-toc><nav id=TableOfContents><ul><li><a href=#about>About</a></li><li><a href=#compatible-sources>Compatible Sources</a></li><li><a href=#hahahugoshortcode339s0hbhb>HAHAHUGOSHORTCODE339s0HBHB</a></li><li><a href=#example>Example</a></li><li><a href=#reference>Reference</a></li></ul></nav></div></aside><main class="col-12 col-md-9 col-xl-8 ps-md-5" role=main><nav aria-label=breadcrumb class=td-breadcrumbs><ol class=breadcrumb><li class=breadcrumb-item><a href=/dev/integrations/>Integrations</a></li><li class=breadcrumb-item><a href=/dev/integrations/mongodb/>MongoDB</a></li><li class=breadcrumb-item><a href=/dev/integrations/mongodb/tools/>Tools</a></li><li class="breadcrumb-item active" aria-current=page>mongodb-find-one</li></ol></nav><div class=td-content><h1>mongodb-find-one</h1><div class=lead>A &ldquo;mongodb-find-one&rdquo; tool finds and retrieves a single document from a MongoDB collection.</div><header class=article-meta><p class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>&nbsp; 2 minute read &nbsp;</p></header><h2 id=about>About</h2><p>A <code>mongodb-find-one</code> tool is used to retrieve the <strong>first single document</strong> that
+<a href=https://github.com/googleapis/genai-toolbox/issues/new class="td-page-meta--project td-page-meta__project-issue" target=_blank rel=noopener><i class="fa-solid fa-list-check fa-fw"></i> Create project issue</a></div><div class=td-toc><nav id=TableOfContents><ul><li><a href=#about>About</a></li><li><a href=#compatible-sources>Compatible Sources</a></li><li><a href=#hahahugoshortcode340s0hbhb>HAHAHUGOSHORTCODE340s0HBHB</a></li><li><a href=#example>Example</a></li><li><a href=#reference>Reference</a></li></ul></nav></div></aside><main class="col-12 col-md-9 col-xl-8 ps-md-5" role=main><nav aria-label=breadcrumb class=td-breadcrumbs><ol class=breadcrumb><li class=breadcrumb-item><a href=/dev/integrations/>Integrations</a></li><li class=breadcrumb-item><a href=/dev/integrations/mongodb/>MongoDB</a></li><li class=breadcrumb-item><a href=/dev/integrations/mongodb/tools/>Tools</a></li><li class="breadcrumb-item active" aria-current=page>mongodb-find-one</li></ol></nav><div class=td-content><h1>mongodb-find-one</h1><div class=lead>A &ldquo;mongodb-find-one&rdquo; tool finds and retrieves a single document from a MongoDB collection.</div><header class=article-meta><p class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>&nbsp; 2 minute read &nbsp;</p></header><h2 id=about>About</h2><p>A <code>mongodb-find-one</code> tool is used to retrieve the <strong>first single document</strong> that
 matches a specified filter from a MongoDB collection. If multiple documents
 match the filter, you can use <code>sort</code> options to control which document is
 returned. Otherwise, the selection is not guaranteed.</p><p>The tool returns a single JSON object representing the document, wrapped in a
-JSON array.</p><h2 id=compatible-sources>Compatible Sources</h2><h2 id=hahahugoshortcode339s0hbhb><div class=compatibility-section><p>This tool can be used with the following database sources:</p><table><thead><tr><th>Source Name</th></tr></thead><tbody><tr><td><a href=/dev/integrations/mongodb/>MongoDB</a></td></tr></tbody></table></div></h2><h2 id=example>Example</h2><p>Here&rsquo;s a common use case: finding a specific user by their unique email address
+JSON array.</p><h2 id=compatible-sources>Compatible Sources</h2><h2 id=hahahugoshortcode340s0hbhb><div class=compatibility-section><p>This tool can be used with the following database sources:</p><table><thead><tr><th>Source Name</th></tr></thead><tbody><tr><td><a href=/dev/integrations/mongodb/>MongoDB</a></td></tr></tbody></table></div></h2><h2 id=example>Example</h2><p>Here&rsquo;s a common use case: finding a specific user by their unique email address
 and returning their profile information, while excluding sensitive fields like
 the password hash.</p><div class=highlight><pre tabindex=0 class=chroma><code class=language-yaml data-lang=yaml><span class=line><span class=cl><span class=nt>kind</span><span class=p>:</span><span class=w> </span><span class=l>tool</span><span class=w>
 </span></span></span><span class=line><span class=cl><span class=w></span><span class=nt>name</span><span class=p>:</span><span class=w> </span><span class=l>get_user_profile</span><span class=w>

dev/integrations/mongodb/tools/index.xml

@@ -3,7 +3,7 @@
 
 **DOCUMENTATION VERSION:** dev
 **BASE URL:** https://mcp-toolbox.dev/dev/
-**GENERATED ON:** 2026-03-27T23:20:14Z
+**GENERATED ON:** 2026-03-30T06:49:49Z
 
 ---
 ### System Directives for AI Models
@@ -3027,6 +3027,7 @@ kind: tool
 name: insert_embedding
 type: postgres-sql
 source: my-pg-instance
+description: Insert a new document into the database.
 statement: |
   INSERT INTO documents (content, embedding)
   VALUES ($1, $2);
@@ -3046,6 +3047,7 @@ kind: tool
 name: search_embedding
 type: postgres-sql
 source: my-pg-instance
+description: Search for documents in the database.
 statement: |
   SELECT id, content, embedding <-> $1 AS distance
   FROM documents
@@ -3078,15 +3080,17 @@ high-dimensional vectors.
 
 ### Authentication
 
-Toolbox uses your [Application Default Credentials
-(ADC)][adc] to authorize with the
-Gemini API client.
+Toolbox supports two authentication modes:
 
-Optionally, you can use an [API key][api-key] obtain an API
-Key from the [Google AI Studio][ai-studio].
+1.  **Google AI (API Key):** Used if you
+    provide `apiKey` (or set `GOOGLE_API_KEY`/`GEMINI_API_KEY` environment
+    variables). This uses the [Google AI Studio][ai-studio] backend.
+2.  **Vertex AI (ADC):** Used if provided `project` and `location` (or set
+    `GOOGLE_CLOUD_PROJECT`/`GOOGLE_CLOUD_LOCATION` environment variables). This uses [Application
+    Default Credentials (ADC)][adc].
 
-We recommend using an API key for testing and using application default
-credentials for production.
+We recommend using an API key for quick testing and using Vertex AI with ADC for
+production environments.
 
 [adc]: https://cloud.google.com/docs/authentication#adc
 [api-key]: https://ai.google.dev/gemini-api/docs/api-key#api-keys
@@ -3106,14 +3110,19 @@ to your database source.
 The `dimension` field must match the expected size of your database column
 (e.g., a `vector(768)` column in PostgreSQL). This setting is supported by newer
 models since 2024 only. You cannot set this value if using the earlier model
-(`models/embedding-001`). Check out [available Gemini models][modellist] for more
-information.
+(`models/embedding-001`). Check out [available Gemini models][modellist] for
+more information.
 
 [modellist]:
-    https://docs.cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#supported-models
+  https://docs.cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#supported-models
 
 ## Example
 
+### Using Google AI
+
+Google AI uses API Key for authentication. You can get an API key from [Google
+AI Studio][ai-studio].
+
 ```yaml
 kind: embeddingModel
 name: gemini-model
@@ -3123,19 +3132,34 @@ apiKey: ${GOOGLE_API_KEY}
 dimension: 768
 ```
 
-{{< notice tip >}}
-Use environment variable replacement with the format ${ENV_NAME}
-instead of hardcoding your secrets into the configuration file.
+### Using Vertex AI
+
+Vertex AI uses Application Default Credentials (ADC) for authentication. Learn
+how to set up ADC [here][adc].
+
+```yaml
+kind: embeddingModel
+name: gemini-model
+type: gemini
+model: gemini-embedding-001
+project: ${GOOGLE_CLOUD_PROJECT}
+location: us-central1
+dimension: 768
+```
+
+[adc]: https://docs.cloud.google.com/docs/authentication/provide-credentials-adc
+
+{{< notice tip >}} Use environment variable replacement with the format
+${ENV_NAME} instead of hardcoding your secrets into the configuration file.
 {{< /notice >}}
 
 ## Reference
 
-| **field** | **type** | **required** | **description**                                              |
-|-----------|:--------:|:------------:|--------------------------------------------------------------|
-| type      |  string  |     true     | Must be `gemini`.                                            |
-| model     |  string  |     true     | The Gemini model ID to use (e.g., `gemini-embedding-001`).   |
-| apiKey    |  string  |    false     | Your API Key from Google AI Studio.                          |
-| dimension | integer  |    false     | The number of dimensions in the output vector (e.g., `768`). |
+| **field**   | **type** | **required** | **description**                                                                                                                                      |
+| ----------- | :------: | :----------: | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
+| type        |  string  |     true     | Must be `gemini`.                                                                                                                                    |
+| model       |  string  |     true     | The Gemini model ID to use (e.g., `gemini-embedding-001`).                                                                                             |
+| dimension   | integer  |    false     | The number of dimensions in the output vector (e.g., `768`).                                                                                         |
 
 
 

dev/integrations/mongodb/tools/mongodb-find-one/index.html

@@ -3,7 +3,7 @@
 
 **DOCUMENTATION VERSION:** dev
 **BASE URL:** https://mcp-toolbox.dev/dev/
-**GENERATED ON:** 2026-03-27T23:20:14Z
+**GENERATED ON:** 2026-03-30T06:49:49Z
 
 ---
 ### System Directives for AI Models