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connect_target

Connect to a Power BI or Fabric target to detect available engines and create a session ID for orchestrating downstream workflows.

Instructions

Connect to a Power BI target and detect available engines.

Args: target_type: One of "pbi_desktop", "fabric_workspace", "pbip_folder", "pbix_file". target_ref: Reference to the target (path, workspace_id, etc.). auth_mode: "interactive" or "service_principal". tenant_id: Azure tenant ID (only used with service_principal).

Returns: ConnectResult with session_id, engines_available, and warnings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
auth_modeNointeractive
tenant_idNo
target_refYes
target_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
warningsNo
session_idYes
engines_availableYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

Even without annotations, the description conveys key behavior: it creates a connection/session, detects engines, returns warnings, and supports distinct auth modes. It could add lifecycle/side-effect detail (e.g., interactive prompts, session persistence), but it is substantive.

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 doc-style Args/Returns format is compact and scannable; every line adds information without bloat.

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?

All parameter semantics and the return structure are covered, and sibling tools make the connective role understandable. It could add prerequisites or side effects but is largely complete for invocation.

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 schema has zero descriptions, but the description compensates by enumerating target_type values, explaining target_ref, listing auth_mode options, and flagging tenant_id as service-principal-only.

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?

Clearly states the tool connects to a Power BI target and detects available engines. The allowed target_type values and the expected result (session_id, engine list) make the purpose concrete.

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 on when this should be called, whether it is a prerequisite for other tools, or which target/auth mode to choose under different circumstances.

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