Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) Queries executed using the `execute_sql_readonly` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.

Designer Tool - Element tool to perform actions like get all elements, get selected element, select element on current active page. and more

The Credentials Search API allows you to find and filter credentials using structured queries with logical operators, sorting, and pagination. See the documentation for more details and examples on how to create queries: https://developers.certifier.io/reference/search-credentials

WHEN: developer needs correct X++ select or T-SQL for D365 tables with proper joins. Triggers: 'X++ select', 'generate a query', 'SQL for', 'join with', 'how to query', 'générer une requête', 'write a select statement', 'select from', 'X++ query for', 'requête X++', 'écrire une select'. Generate both X++ select statements and equivalent T-SQL queries for D365 F&O tables. Uses real field names, relations, and indexes from the knowledge base to produce correct joins. Supports: field selection, multi-table joins (auto-detects relations), WHERE filters, ORDER BY, TOP/firstonly, cross-company. Also accepts natural language descriptions like 'find all open sales orders for customer 1001 with CustTable join'. [!] For multi-table joins, call find_related_objects (or get_relation_graph if the relation index is loaded) FIRST to get the correct FK relations -- this tool will then produce accurate join conditions. [!] The generated X++ is a template -- adapt it to your custom code context before using in production. Returns side-by-side X++ and SQL with explanations.

Execute a SQL query on a site's database. Supports SELECT, INSERT, UPDATE, DELETE, and DDL statements. Results are limited to 1000 rows for SELECT queries. Requires: API key with write scope. Args: slug: Site identifier database: Database name query: SQL query string Returns: {"columns": ["id", "title"], "rows": [[1, "Hello"], ...], "affected_rows": 0, "query_time_ms": 12}

Search the Wherobots documentation. ⚠️ WORKFLOW: This should be your FIRST tool call for any query task. Before writing queries, ALWAYS search documentation to understand: - Available spatial functions and their syntax - Best practices and common patterns - Example queries for similar use cases 📋 NEXT STEPS after this tool: 1. Use catalog tools (list_catalogs, list_databases, list_tables) to find data 2. Use describe_table_tool to understand table schemas 3. Use generate_spatial_query_tool to create SQL queries This tool searches the official Wherobots documentation to find relevant information about spatial functions, data formats, best practices, and more. It's useful when users need help understanding Wherobots features or syntax. Parameters ---------- query : str The search query string ctx : Context FastMCP context (injected automatically) page_size : int | None Optional number of results to return (default: 10) Returns ------- DocumentationSearchOutput A structured object containing documentation search results. - 'results': List of DocumentResult objects, each containing: - 'content': The documentation content snippet - 'path': The URL path to the documentation page - 'metadata': Additional metadata about the result Example Usage for LLM: - When user asks about Wherobots features, functions, or syntax - When generating queries and need context about spatial functions - Example User Queries and corresponding Tool Calls: - User: "How do I use ST_INTERSECTS in Wherobots?" - Tool Call: search_documentation("ST_INTERSECTS spatial function") - User: "What spatial functions are available for distance calculations?" - Tool Call: search_documentation("spatial distance functions") - User: "How do I connect to Wherobots from Python?" - Tool Call: search_documentation("Python connection API") For the best way to understand how to use spatial SQL in Wherobots, refer to the following Wherobots Documentation: - Writing Effective Spatial Queries in Wherobots: https://docs.wherobots.com/develop/write-spatial-queries - Writing Effective Spatial Queries in Wherobots complete example sub-section: https://docs.wherobots.com/develop/write-spatial-queries#complete-example:-counting-baseball-stadiums-in-the-us The complete example demonstrates: - Starting the `SedonaContext` - Using `wkls` where applicable - Loading spatial data from Wherobots tables - Using the correct schema from wherobots_open_data spatial catalogs - Writing spatial SQL queries effectively