pepe57 · pull · Apr 10, 2026 · Apr 10, 2026 · Apr 10, 2026 · Apr 10, 2026
diff --git a/MCP-TOOLBOX-EXTENSION.md b/MCP-TOOLBOX-EXTENSION.md
@@ -166,16 +166,16 @@ instances, often by analyzing metrics and logs.
 
 ### Other Google Cloud Data Services
 
-#### Dataplex
+#### Knowledge Catalog (formerly known as Dataplex)
 
-* For interacting with Dataplex data lakes and assets:
+* For interacting with Knowledge Catalog data lakes and assets:
 
     ```bash
-    gemini extensions install https://github.com/gemini-cli-extensions/dataplex
+    gemini extensions install https://github.com/gemini-cli-extensions/knowledge-catalog
     ```
 
     Configuration:
-    https://github.com/gemini-cli-extensions/dataplex/tree/main?tab=readme-ov-file#configuration
+    https://github.com/gemini-cli-extensions/knowledge-catalog/tree/main?tab=readme-ov-file#configuration
 
 #### Looker
 

diff --git a/README.md b/README.md
@@ -97,7 +97,7 @@ You can use the Toolbox in any MCP-compatible IDE or client (e.g., Gemini CLI, G
 When you run Toolbox with a `--prebuilt=<database>` flag, you instantly get access to standard tools to interact with that database. 
 
 Supported databases currently include:
-- **Google Cloud:** AlloyDB, BigQuery, Cloud SQL (PostgreSQL, MySQL, SQL Server), Spanner, Firestore, Dataplex
+- **Google Cloud:** AlloyDB, BigQuery, Cloud SQL (PostgreSQL, MySQL, SQL Server), Spanner, Firestore, Knowledge Catalog (formerly known as Dataplex).
 - **Other Databases:** PostgreSQL, MySQL, SQL Server, Oracle, MongoDB, Redis, Elasticsearch, CockroachDB, ClickHouse, Couchbase, Neo4j, Snowflake, Trino, and more.
 
 For a full list of available tools and their capabilities across all supported databases, see the [Prebuilt Tools Reference](https://mcp-toolbox.dev/documentation/configuration/prebuilt-configs/).

diff --git a/cmd/internal/config_test.go b/cmd/internal/config_test.go
@@ -1747,6 +1747,10 @@ func TestPrebuiltTools(t *testing.T) {
 					Name:      "replication",
 					ToolNames: []string{"replication_stats", "list_replication_slots", "list_publication_tables", "list_roles", "list_pg_settings", "database_overview"},
 				},
+				"vectorassist": {
+					Name:      "vectorassist",
+					ToolNames: []string{"execute_sql", "define_spec", "modify_spec", "apply_spec", "generate_query"},
+				},
 			},
 		},
 		{

diff --git a/cmd/internal/imports.go b/cmd/internal/imports.go
@@ -79,6 +79,10 @@ import (
 	_ "github.com/googleapis/mcp-toolbox/internal/tools/cloudsqlmysql/cloudsqlmysqlcreateinstance"
 	_ "github.com/googleapis/mcp-toolbox/internal/tools/cloudsqlpg/cloudsqlpgcreateinstances"
 	_ "github.com/googleapis/mcp-toolbox/internal/tools/cloudsqlpg/cloudsqlpgupgradeprecheck"
+	_ "github.com/googleapis/mcp-toolbox/internal/tools/cloudsqlpg/vectorassistapplyspec"
+	_ "github.com/googleapis/mcp-toolbox/internal/tools/cloudsqlpg/vectorassistdefinespec"
+	_ "github.com/googleapis/mcp-toolbox/internal/tools/cloudsqlpg/vectorassistgeneratequery"
+	_ "github.com/googleapis/mcp-toolbox/internal/tools/cloudsqlpg/vectorassistmodifyspec"
 	_ "github.com/googleapis/mcp-toolbox/internal/tools/cockroachdb/cockroachdbexecutesql"
 	_ "github.com/googleapis/mcp-toolbox/internal/tools/cockroachdb/cockroachdblistschemas"
 	_ "github.com/googleapis/mcp-toolbox/internal/tools/cockroachdb/cockroachdblisttables"

diff --git a/docs/CLOUDSQLPG_README.md b/docs/CLOUDSQLPG_README.md
@@ -72,6 +72,10 @@ The Cloud SQL for PostgreSQL MCP server provides the following tools:
 | `list_triggers`                  | Lists all non-internal triggers in a database.                 |
 | `list_indexes`                   | Lists available user indexes in the database.                  |
 | `list_sequences`                 | Lists sequences in the database.                               |
+| `define_spec`                    | Defines a new vector specification for search workloads.       |
+| `modify_spec`                    | Modifies an existing vector specification.                     |
+| `apply_spec`                     | Executes SQL recommendations for a vector specification.       | 
+| `generate_query`                 | Generates optimized SQL queries for vector searches.           |
 
 
 ## Custom MCP Server Configuration

diff --git a/docs/DATAPLEX_README.md → docs/KNOWLEDGE_CATALOG_README.md b/docs/DATAPLEX_README.md → docs/KNOWLEDGE_CATALOG_README.md
@@ -1,12 +1,12 @@
-# Dataplex MCP Server
+# Knowledge Catalog MCP Server
 
-The Dataplex Model Context Protocol (MCP) Server gives AI-powered development tools the ability to work with your Google Cloud Dataplex Catalog. It supports searching and looking up entries and aspect types.
+The Knowledge Catalog (formerly known as Dataplex) Model Context Protocol (MCP) Server gives AI-powered development tools the ability to work with your Google Cloud Knowledge Catalog. It supports searching and looking up entries and aspect types.
 
 ## Features
 
-An editor configured to use the Dataplex MCP server can use its AI capabilities to help you:
+An editor configured to use the Knowledge Catalog MCP server can use its AI capabilities to help you:
 
-- **Search Catalog** - Search for entries in Dataplex Catalog
+- **Search Catalog** - Search for entries in Knowledge Catalog
 - **Explore Metadata** - Lookup specific entries and search aspect types
 
 ## Prerequisites
@@ -37,18 +37,18 @@ You'll now be able to see all enabled tools in the "Tools" tab.
 
 ## Usage
 
-Once configured, the MCP server will automatically provide Dataplex capabilities to your AI assistant. You can:
+Once configured, the MCP server will automatically provide Knowledge Catalog capabilities to your AI assistant. You can:
 
-*   "Search for entries related to 'sales' in Dataplex."
+*   "Search for entries related to 'sales' in Knowledge Catalog."
 *   "Look up details for the entry 'projects/my-project/locations/us-central1/entryGroups/my-group/entries/my-entry'."
 
 ## Server Capabilities
 
-The Dataplex MCP server provides the following tools:
+The Knowledge Catalog MCP server provides the following tools:
 
 | Tool Name             | Description                                                                                                                  |
 |:----------------------|:-----------------------------------------------------------------------------------------------------------------------------|
-| `search_entries`      | Search for entries in Dataplex Catalog.                                                                                      |
+| `search_entries`      | Search for entries in Knowledge Catalog.                                                                                      |
 | `lookup_entry`        | Retrieve specific subset of metadata (for example, schema, usage, business overview, and contacts) of a specific data asset. |
 | `search_aspect_types` | Find aspect types relevant to the query.                                                                                     |
 | `lookup_context`      | Retrieve rich metadata regarding one or more data assets along with their relationships.                                     |
@@ -79,4 +79,4 @@ Add the following configuration to your MCP client (e.g., `settings.json` for Ge
 
 ## Documentation
 
-For more information, visit the [Dataplex documentation](https://cloud.google.com/dataplex/docs).
+For more information, visit the [Knowledge Catalog documentation](https://cloud.google.com/dataplex/docs).
diff --git a/docs/en/documentation/connect-to/gemini-cli/_index.md b/docs/en/documentation/connect-to/gemini-cli/_index.md
@@ -36,7 +36,7 @@ Below are a list of Gemini CLI Extensions powered by MCP Toolbox:
 * [cloud-sql-postgresql-observability](https://github.com/gemini-cli-extensions/cloud-sql-postgresql-observability)
 * [cloud-sql-sqlserver](https://github.com/gemini-cli-extensions/cloud-sql-sqlserver)
 * [cloud-sql-sqlserver-observability](https://github.com/gemini-cli-extensions/cloud-sql-sqlserver-observability)
-* [dataplex](https://github.com/gemini-cli-extensions/dataplex)
+* [knowledge-catalog](https://github.com/gemini-cli-extensions/knowledge-catalog)
 * [firestore-native](https://github.com/gemini-cli-extensions/firestore-native)
 * [looker](https://github.com/gemini-cli-extensions/looker)
 * [mcp-toolbox](https://github.com/gemini-cli-extensions/mcp-toolbox)

diff --git a/docs/en/documentation/introduction/_index.md b/docs/en/documentation/introduction/_index.md
@@ -77,7 +77,7 @@ Set the appropriate environment variables to connect, see the [Prebuilt Tools Re
 When you run Toolbox with a `--prebuilt=<database>` flag, you instantly get access to standard tools to interact with that database. 
 
 Supported databases currently include:
-- **Google Cloud:** AlloyDB, BigQuery, Cloud SQL (PostgreSQL, MySQL, SQL Server), Spanner, Firestore, Dataplex
+- **Google Cloud:** AlloyDB, BigQuery, Cloud SQL (PostgreSQL, MySQL, SQL Server), Spanner, Firestore, Knowledge Catalog (formerly known as Dataplex).
 - **Other Databases:** PostgreSQL, MySQL, SQL Server, Oracle, MongoDB, Redis, Elasticsearch, CockroachDB, ClickHouse, Couchbase, Neo4j, Snowflake, Trino, and more.
 
 For a full list of available tools and their capabilities across all supported databases, see the [Prebuilt Tools Reference](https://mcp-toolbox.dev/documentation/configuration/prebuilt-configs/).

diff --git a/docs/en/integrations/bigquery/tools/bigquery-search-catalog.md b/docs/en/integrations/bigquery/tools/bigquery-search-catalog.md
@@ -8,7 +8,7 @@ description: >
 
 ## About
 
-A `bigquery-search-catalog` tool returns all entries in Dataplex Catalog (e.g.
+A `bigquery-search-catalog` tool returns all entries in Knowledge Catalog (e.g.
 tables, views, models) with system=bigquery that matches given user query.
 
 `bigquery-search-catalog` takes a required `query` parameter based on which
@@ -30,14 +30,14 @@ following parameters:
 ### IAM Permissions
 
 Bigquery uses [Identity and Access Management (IAM)][iam-overview] to control
-user and group access to Dataplex resources. Toolbox will use your
+user and group access to Knowledge Catalog (formerly known as Dataplex) resources. Toolbox will use your
 [Application Default Credentials (ADC)][adc] to authorize and authenticate when
-interacting with [Dataplex][dataplex-docs].
+interacting with [Knowledge Catalog][dataplex-docs].
 
 In addition to [setting the ADC for your server][set-adc], you need to ensure
 the IAM identity has been given the correct IAM permissions for the tasks you
-intend to perform. See [Dataplex Universal Catalog IAM permissions][iam-permissions]
-and [Dataplex Universal Catalog IAM roles][iam-roles] for more information on
+intend to perform. See [Knowledge Catalog IAM permissions][iam-permissions]
+and [Knowledge Catalog IAM roles][iam-roles] for more information on
 applying IAM permissions and roles to an identity.
 
 [iam-overview]: https://cloud.google.com/dataplex/docs/iam-and-access-control

diff --git a/docs/en/integrations/cloud-sql-pg/tools/vector-assist-apply-spec.md b/docs/en/integrations/cloud-sql-pg/tools/vector-assist-apply-spec.md
@@ -0,0 +1,59 @@
+---
+title: "vector-assist-apply-spec"
+type: docs
+weight: 1
+description: >
+  The "vector-assist-apply-spec" tool automatically executes all SQL recommendations
+  associated with a specific vector specification or table to finalize the
+  vector search setup.
+---
+
+## About
+
+The `vector-assist-apply-spec` tool automatically executes all the SQL recommendations associated with a specific vector specification (spec_id) or table. It runs the necessary commands in the correct sequence to provision the workload, marking each step as applied once successful. 
+
+Use this tool when the user has reviewed the generated recommendations from a defined (or modified) spec and is ready to apply the changes directly to their database instance to finalize the vector search setup. Under the hood, this tool connects to the target database and executes the `vector_assist.apply_spec` function.
+
+## Compatible Sources
+
+{{< compatible-sources >}}
+
+## Requirements
+
+{{< notice tip >}} 
+Ensure that your target PostgreSQL database has the required `vector_assist` extension installed, in order for this tool to execute successfully.
+{{< /notice >}}
+
+## Parameters
+
+The tool takes the following input parameters:
+
+| Parameter     | Type   | Description                                                           | Required |
+| :------------ | :----- | :-------------------------------------------------------------------- | :------- |
+| `spec_id`     | string | Unique ID of the vector specification to apply.                       | No       |
+| `table_name`  | string | Target table name for applying the vector specification.              | No       |
+| `column_name` | string | Text or vector column name to uniquely identify the specification.    | No       |
+| `schema_name` | string | Schema name for the target table.                                     | No       |
+
+> Note
+> Parameters are marked as required or optional based on the vector assist function definitions. 
+> The function may perform further validation on optional parameters to ensure all necessary 
+> data is available before returning a response.
+
+## Example
+
+```yaml
+kind: tool
+name: apply_spec
+type: vector-assist-apply-spec
+source: my-database-source
+description: "This tool automatically executes all the SQL recommendations associated with a specific vector specification (spec_id) or table. It runs the necessary commands in the correct sequence to provision the workload, marking each step as applied once successful. Use this tool when the user has reviewed the generated recommendations from a defined (or modified) spec and is ready to apply the changes directly to their database instance to finalize the vector search setup."
+```
+
+## Reference
+
+| **field**   | **type** | **required** | **description**                                      |
+|-------------|:--------:|:------------:|------------------------------------------------------|
+| type        |  string  |     true     | Must be "vector-assist-apply-spec".                 |
+| source      |  string  |     true     | Name of the source the SQL should execute on.        |
+| description |  string  |    false     | Description of the tool that is passed to the agent. |
diff --git a/docs/en/integrations/cloud-sql-pg/tools/vector-assist-define-spec.md b/docs/en/integrations/cloud-sql-pg/tools/vector-assist-define-spec.md
@@ -0,0 +1,73 @@
+---
+title: "vector-assist-define-spec"
+type: docs
+weight: 1
+description: >
+  The "vector-assist-define-spec" tool defines a new vector specification by
+  capturing the user's intent and requirements for a vector search workload,
+  generating SQL recommendations for setting up database, embeddings, and
+  vector indexes.
+---
+
+## About
+
+The `vector-assist-define-spec` tool defines a new vector specification by capturing the user's intent and requirements for a vector search workload. It generates a complete, ordered set of SQL recommendations required to set up the database, embeddings, and vector indexes. 
+
+Use this tool at the very beginning of the vector setup process when an agent or user first wants to configure a table for vector search, generate embeddings, or create a new vector index. Under the hood, this tool connects to the target database and executes the `vector_assist.define_spec` function to generate the necessary specifications.
+
+## Compatible Sources
+
+{{< compatible-sources >}}
+
+## Requirements
+
+{{< notice tip >}} 
+Ensure that your target PostgreSQL database has the required `vector_assist` extension installed, in order for this tool to execute successfully.
+{{< /notice >}}
+
+## Parameters
+
+The tool takes the following input parameters:
+
+| Parameter                | Type    | Description                                                            | Required |
+| :----------------------- | :------ | :--------------------------------------------------------------------- | :------- |
+| `table_name`             | string  | Target table name for setting up the vector workload.                  | Yes      |
+| `schema_name`            | string  | Name of the schema containing the target table.                        | No       |
+| `spec_id`                | string  | Unique ID for the vector specification; auto-generated if omitted.     | No       |
+| `vector_column_name`     | string  | Name of the column containing the vector embeddings.                   | No       |
+| `text_column_name`       | string  | Name of the text column for setting up vector search.                  | No       |
+| `vector_index_type`      | string  | Type of vector index ('hnsw', 'ivfflat', or 'scann').                  | No       |
+| `embeddings_available`   | boolean | Indicates if vector embeddings already exist in the table.             | No       |
+| `num_vectors`            | integer | Expected total number of vectors in the dataset.                       | No       |
+| `dimensionality`         | integer | Dimension of existing vectors or the chosen embedding model.           | No       |
+| `embedding_model`        | string  | Model to be used for generating vector embeddings.                     | No       |
+| `prefilter_column_names` | array   | List of columns to use for prefiltering vector queries.                | No       |
+| `distance_func`          | string  | Distance function for comparing vectors ('cosine', 'ip', 'l2', 'l1').  | No       |
+| `quantization`           | string  | Quantization method for vector indexes ('none', 'halfvec', 'bit').     | No       |
+| `memory_budget_kb`       | integer | Maximum memory (in KB) the index can use during build.                 | No       |
+| `target_recall`          | float   | Target recall rate for standard vector queries using this index.       | No       |
+| `target_top_k`           | integer | Number of top results (top-K) to retrieve per query.                   | No       |
+| `tune_vector_index`      | boolean | Indicates whether automatic tuning is required for the index.          | No       |
+
+> Note
+> Parameters are marked as required or optional based on the vector assist function definitions. 
+> The function may perform further validation on optional parameters to ensure all necessary 
+> data is available before returning a response.
+
+## Example
+
+```yaml
+kind: tool
+name: define_spec
+type: vector-assist-define-spec
+source: my-database-source
+description: "This tool defines a new vector specification by capturing the user's intent and requirements for a vector search workload. This generates a complete, ordered set of SQL recommendations required to set up the database, embeddings, and vector indexes. Use this tool at the very beginning of the vector setup process when a user first wants to configure a table for vector search, generate embeddings, or create a new vector index."
+```
+
+## Reference
+
+| **field**   | **type** | **required** | **description**                                      |
+|-------------|:--------:|:------------:|------------------------------------------------------|
+| type        |  string  |     true     | Must be "vector-assist-define-spec".                 |
+| source      |  string  |     true     | Name of the source the SQL should execute on.        |
+| description |  string  |    false     | Description of the tool that is passed to the agent. |