manage_aws_athena_named_queries

Manage saved SQL queries in AWS Athena. This tool provides operations for creating, retrieving, updating, and deleting named queries in AWS Athena. Named queries are saved SQL statements that can be easily reused, shared with team members, and executed without having to rewrite complex queries. ## Requirements - The server must be run with the `--allow-write` flag for create-named-query, delete-named-query, and update-named-query operations - Appropriate AWS permissions for Athena named query operations ## Operations - **batch-get-named-query**: Get details for up to 50 named queries by their IDs - **create-named-query**: Save a new SQL query with a name and description - **delete-named-query**: Remove a saved query - **get-named-query**: Retrieve a single named query by ID - **list-named-queries**: List available named query IDs - **update-named-query**: Modify an existing named query ## Example ```python # Create a named query create_response = await manage_aws_athena_named_queries( operation='create-named-query', name='Daily Active Users', description='Query to calculate daily active users', database='analytics', query_string='SELECT date, COUNT(DISTINCT user_id) AS active_users FROM user_events GROUP BY date ORDER BY date DESC', work_group='primary', ) # Later, retrieve the named query query = await manage_aws_athena_named_queries( operation='get-named-query', named_query_id=create_response.named_query_id ) ``` Args: ctx: MCP context operation: Operation to perform named_query_id: ID of the named query named_query_ids: List of named query IDs (max 50) name: Name of the named query description: Description of the named query (max 1024 chars) database: Database context for the named query query_string: The SQL query string client_request_token: Unique token for idempotent requests work_group: The name of the workgroup max_results: Maximum number of results to return next_token: Pagination token Returns: Union of response types specific to the operation performed. It is categorised as a Write tool in the Amazon Data Processing MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

Add a rule in your Intercept YAML policy under the tools section for manage_aws_athena_named_queries. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Amazon Data Processing MCP Server MCP server.

manage_aws_athena_named_queries is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Yes. Add a rate_limit block to the manage_aws_athena_named_queries rule in your Intercept policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

Set action: deny in the Intercept policy for manage_aws_athena_named_queries. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

manage_aws_athena_named_queries is provided by the Amazon Data Processing MCP Server MCP server (awslabs.aws-dataprocessing-mcp-server). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.