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sdebruyn

fabric-dw-mcp-cli

by sdebruyn

refresh_sql_endpoint_metadata

Refreshes metadata of a SQL analytics endpoint by syncing from the underlying Lakehouse. Supports optional table recreation to resolve inconsistencies.

Instructions

Refresh metadata for a SQL analytics endpoint (sync from the underlying Lakehouse).

This is a long-running operation (LRO) that is polled to completion. Returns a list of per-table sync results.

Args: workspace: Workspace name or GUID. endpoint: SQL analytics endpoint name or GUID. recreate_tables: When True, drop and recreate all tables during the refresh. Use to resolve inconsistencies or force a clean rebuild. Destructive — use with caution. Requires FABRIC_MCP_ALLOW_DESTRUCTIVE=1 when enabled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceYes
endpointYes
recreate_tablesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses the long-running nature, polling, and destructive behavior of `recreate_tables`, but does not specify availability of the endpoint during operation or polling details.

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

Conciseness4/5

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

The description is concise with no fluff, uses bullet points for arguments, and is front-loaded with the core purpose. However, it could be slightly more structured with clear sections.

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?

For a tool with 3 parameters and LRO behavior, the description covers the key aspects: purpose, LRO polling, parameter details, and return type. With an output schema present, the description is adequately complete.

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

Parameters4/5

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

Schema coverage is 0%, so description must compensate. It explains `workspace` and `endpoint` as name or GUID, and provides detailed semantics for `recreate_tables`, including its purpose, destructive effect, and required environment variable.

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 the verb 'refresh' and resource 'metadata for a SQL analytics endpoint'. It distinguishes from sibling tools, as no other sibling tool specifically performs a refresh operation.

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 mentions it is a long-running operation that is polled to completion and provides guidance on the destructive `recreate_tables` parameter, but does not explicitly state when to use this tool versus alternatives or when not to use it.

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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