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HorizunGroup

Horizun PBI MCP

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by HorizunGroup

pbi_prepare_delivery

Audit your Power BI project, generate a pre-delivery blocker checklist, and apply automatic fixes to improve the delivery score before and after comparison.

Instructions

Checklist de pre-entrega con plan de correccion.

Audita el proyecto, produce un checklist de bloqueantes y propone las correcciones automaticas disponibles. Con dry_run=false las aplica y compara el puntaje antes y despues.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNo
request_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false. The description adds valuable context: it explains that with dry_run=false the tool applies corrections and compares scores before/after. This discloses the mutating behavior and the dry-run safeguard, going beyond the annotations.

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 brief (three sentences), front-loaded with a clear summary ('Checklist de pre-entrega con plan de correccion'), and contains no redundant information. Every sentence adds functional value.

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 that an output schema exists, the description does not need to explain return values. It covers the core workflow (audit, checklist, corrections, optional apply) and the dry_run behavior. However, it omits details about request_id and potential side effects of applying corrections, leaving minor gaps.

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

Parameters3/5

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

The description explicitly explains the dry_run parameter (with dry_run=false it applies corrections), which is key. However, it does not mention request_id at all, and since schema_description_coverage is 0%, the description only partially compensates for the undocumented parameters.

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 tool's function: it audits the project, produces a checklist of blockers, proposes automatic corrections, and optionally applies them when dry_run=false. It uses a specific verb ('Audita', 'produce') and distinguishes itself from siblings like pbi_audit_project and pbi_apply_audit_fixes by combining audit and correction application.

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

Usage Guidelines4/5

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

The description clearly implies a pre-delivery usage context ('Checklist de pre-entrega') and explains the workflow with dry_run. However, it does not explicitly mention when to choose this tool over alternatives such as pbi_audit_project or pbi_apply_audit_fixes, so it lacks explicit exclusions.

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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