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sdebruyn

fabric-dw-mcp-cli

by sdebruyn

list_sql_pool_insights

Fetch SQL pool insight events for a Microsoft Fabric warehouse. Specify workspace and warehouse, and optionally set a time range or row limit to get actionable performance data.

Instructions

Return SQL pool insight events from queryinsights.sql_pool_insights.

Args: workspace: Workspace name or GUID. warehouse: Warehouse or SQL Analytics Endpoint name or GUID. limit: Maximum rows to return (1-10000, default 100). since: Optional ISO-8601 lower bound on timestamp. until: Optional ISO-8601 upper bound on timestamp.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
sinceNo
untilNo
warehouseYes
workspaceYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are present, so the description carries the behavioral disclosure burden. It communicates a read-only retrieval operation and documents parameter-level constraints like limit range and ISO-8601 time bounds, but it does not state ordering, range inclusivity, or any prerequisites. The behavior is reasonably transparent for a list operation.

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 purpose sentence is front-loaded and the Args section is a compact, per-parameter list with no filler. Every line conveys a meaningful semantic or constraint.

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?

With an output schema present and only five simple parameters, the description covers purpose, data source, and all argument semantics. It lacks explicit sibling usage guidance and details like sort order or whether bounds are inclusive, but these are minor for a read-only listing tool.

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 description coverage is 0%, and the description fully compensates: workspace/warehouse accept names or GUIDs, since/until are specified as ISO-8601 timestamp bounds, and limit includes range and default. This adds substantial meaning beyond the bare JSON schema types.

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

Purpose4/5

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

The first sentence states a specific verb ('Return') and a specific resource ('SQL pool insight events from queryinsights.sql_pool_insights'), which is precise and distinct from sibling tools. It does not explicitly call out a sibling or contrast its scope, so it stops short of full differentiation.

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 does not say when to use this tool instead of alternatives like list_request_history, list_running_queries, or list_frequent_queries. Usage context is implied by the specific resource name and source table, but no exclusions or alternative conditions are 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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