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Horizun PBI MCP

pbi_prepare_delivery

Run a pre-delivery audit on Power BI projects to produce a blocker checklist and apply automatic corrections. Compare quality scores before and after to ensure readiness.

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the main behavioral traits: audit, produce checklist, propose corrections, and with dry_run=false, apply them and compare scores. It also implies a write operation when dry_run is false. It does not discuss side effects or reversibility, but it provides a solid behavioral overview.

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 concise and efficiently structured. It opens with a one-line summary, followed by a two-sentence expansion that details the workflow. Every sentence serves a purpose with no redundancy.

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?

The description provides a full workflow overview, including optional application of fixes and score comparison. The presence of an output schema likely covers return values, but the description lacks explicit prerequisites (e.g., requiring an open project), which is a minor gap given the tool's complexity.

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 explains the dry_run parameter's effect ('Con dry_run=false las aplica'), adding meaning beyond the schema. However, request_id is not mentioned at all, and with 0% schema coverage, the description only partially compensates for the missing parameter semantics.

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 defines the tool's purpose: it audits the project, produces a blocker checklist, and proposes or applies automatic corrections depending on the dry_run flag. This specific workflow distinguishes it from sibling tools like pbi_plan_audit_fixes and pbi_apply_audit_fixes, which are more granular.

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 establishes clear usage context: this is a pre-delivery checklist tool that can optionally apply fixes. However, it does not explicitly mention when to use this tool versus the separate audit/plan/apply tools, nor does it state exclusions, so it lacks explicit alternatives.

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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