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sdebruyn

fabric-dw-mcp-cli

by sdebruyn

read_view

Retrieve rows from a Microsoft Fabric view as JSON columns and rows by specifying workspace, endpoint, and qualified view name, with optional point-in-time reads.

Instructions

Return up to count rows from a view as JSON-serialisable columns + rows.

Args: workspace: Workspace name or GUID. item: Warehouse or SQL endpoint name or GUID. qualified_name: Dot-separated qualified view name, e.g. dbo.vw_sales. count: Maximum number of rows to return (1-10000, default 10). as_of: Optional ISO-8601 UTC timestamp for a point-in-time (time-travel) read. When supplied the query uses OPTION (FOR TIMESTAMP AS OF ...). Omit to read the latest data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemYes
as_ofNo
countNo
workspaceYes
qualified_nameYes

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 carries the behavioral burden, and it delivers useful traits: output is JSON-serialisable, rows are capped at count, and as_of triggers a FOR TIMESTAMP AS OF point-in-time query. It does not cover permissions or error behavior, but for a read-only tool with an output schema, the transparency is strong.

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 compact and well-structured: a one-line functional summary followed by a clean Args block. Every sentence adds value, and the most important behavior (count limit, output shape, time-travel) is front-loaded.

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

Completeness5/5

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

Given the output schema exists, the description does not need to detail the exact return structure. All required parameters are explained, the optional parameter is fully specified, default/limits are stated, and the time-travel behavior is disclosed. An agent has everything needed to call it correctly.

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?

The input schema has no property descriptions, but the description documents all five parameters meaningfully: workspace and item accept names or GUIDs, qualified_name is shown with a dot-separated example, count has range/default, and as_of has format plus behavioral semantics. This fully compensates for 0% schema_description_coverage.

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 opening sentence names a specific operation (Return rows), a resource (a view), a limit (count), and a return shape (JSON-serialisable columns + rows). The view+rows wording clearly distinguishes it from many siblings like get_view or list_views, though it does not explicitly contrast with the similar read_table sibling.

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

Usage Guidelines2/5

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

No guidance is given for when to choose read_view over alternatives such as read_table, get_view, or count_view_rows, all of which appear in the sibling list. The only usage context provided is the as_of parameter ('Omit to read the latest data'), which is parameter-level advice, not tool-selection guidance.

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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