Didier Durand
a8f2ddd943
Merge https://github.com/google/adk-python/pull/4175 ### Link to Issue or Description of Change **1. Link to an existing issue (if applicable):** N/A: just fixing typos discovered while reading the repo **2. Or, if no issue exists, describe the change:** No code change, just typo fixes: see commit diffs for all details **Problem:** Trying to improve overall repo quality **Solution:** Fixing typos as they get discovered ### Testing Plan N/A **Unit Tests:** N/A **Manual End-to-End (E2E) Tests:** N/A ### Checklist - [X] I have read the [CONTRIBUTING.md](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) document. - [X] I have performed a self-review of my own code. - [ ] I have commented my code, particularly in hard-to-understand areas. - [ ] I have added tests that prove my fix is effective or that my feature works. - [X] New and existing unit tests pass locally with my changes. - [ ] I have manually tested my changes end-to-end. - [ ] Any dependent changes have been merged and published in downstream modules. COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/4175 from didier-durand:fix-typos-c 16e93ed2d9bc153fa0332ab1ae39633fcc5056e9 PiperOrigin-RevId: 858751240
BigQuery MCP Toolset Sample
Introduction
This sample agent demonstrates using ADK's McpToolset to interact with
BigQuery's official MCP endpoint, allowing an agent to access and execute
tools by leveraging the Model Context Protocol (MCP). These tools include:
list_dataset_ids
Fetches BigQuery dataset ids present in a GCP project.
get_dataset_info
Fetches metadata about a BigQuery dataset.
list_table_ids
Fetches table ids present in a BigQuery dataset.
get_table_info
Fetches metadata about a BigQuery table.
execute_sql
Runs or dry-runs a SQL query in BigQuery.
How to use
Set up your project and local authentication by following the guide Use the BigQuery remote MCP server. This agent uses Application Default Credentials (ADC) to authenticate with the BigQuery MCP endpoint.
Set up environment variables in your .env file for using
Google AI Studio
or
Google Cloud Vertex AI
for the LLM service for your agent. For example, for using Google AI Studio you
would set:
- GOOGLE_GENAI_USE_VERTEXAI=FALSE
- GOOGLE_API_KEY={your api key}
Then run the agent using adk run . or adk web . in this directory.
Sample prompts
- which weather datasets exist in bigquery public data?
- tell me more about noaa_lightning
- which tables exist in the ml_datasets dataset?
- show more details about the penguins table
- compute penguins population per island.