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IBM watsonx.data MCP Server

Official
by IBM

execute_select

Execute read-only SELECT queries on IBM watsonx.data by supplying SQL, catalog, schema, and engine to retrieve query results including columns and rows.

Instructions

Execute read-only SELECT queries against watsonx.data.

Args: sql: SQL SELECT query to execute (must start with SELECT) catalog_name: Target catalog (e.g., "iceberg_data", "tpch") schema_name: Default schema for unqualified table names engine_id: Engine to run query on (from list_engines, must be running) limit: Max rows to return (default: 500 if no LIMIT in query). Note: Using high limits will consume more tokens.

Returns: Dict with: - query_id: Unique query identifier - columns: List of {name, type} objects - rows: List of row data (list of lists) - row_count: Number of rows returned - execution_time_ms: Query duration in milliseconds - catalog_name, schema_name: Echo of inputs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYes
catalog_nameYes
schema_nameYes
engine_idYes
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses read-only behavior and notes that high limits consume more tokens. It also details return format. However, it does not mention error handling or performance constraints beyond token usage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with Args and Returns sections, concise yet informative. Every sentence adds value, and it avoids redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the parameter count and presence of output schema, the description covers all inputs and return values comprehensively. Minor omission: no mention of potential errors or query execution async nature, but overall sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description thoroughly explains each parameter: sql must start with SELECT, catalog_name provides examples, schema_name as default, engine_id from list_engines and must be running, and limit with default behavior. This compensates fully for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it executes read-only SELECT queries against watsonx.data, specifying the verb (execute) and resource (SELECT queries on watsonx.data). This distinguishes it from sibling tools like execute_insert or execute_update, which perform different operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for read-only queries but does not explicitly state when to use this tool versus alternatives like execute_insert, execute_update, or explain_query. No guidance on when-not or comparisons with siblings is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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