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HorizunGroup

Horizun PBI MCP

Official
by HorizunGroup

pbi_repair_broken_references

Detect and fix broken references in Power BI visuals. Without a mapping, it diagnoses; provide a mapping like {"Tabla[Viejo]": "Tabla[Nuevo]"} to apply repairs, with destinations validated against the model.

Instructions

Detecta referencias rotas en los visuales y las repara.

Sin mapping solo diagnostica: adivinar a que campo queria apuntar un visual roto no es una decision que deba tomarse sola. Pasa {"Tabla[Viejo]": "Tabla[Nuevo]"} para repararlas, y el destino se valida contra el modelo antes de escribir.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNo
mappingNo
request_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description adds meaningful behavioral context beyond annotations: the tool acts as a diagnostic-only mode without mapping, and it validates destinations before writing. This conditional behavior is not captured by the annotations, but the description does not elaborate on all side effects (e.g., dry_run behavior).

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, front-loaded with the core purpose, and includes a practical example. Every sentence contributes useful information without unnecessary detail.

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 output schema covers return values, so the description only needs to clarify behavior. It explains the two modes, the validation step, and the mapping format. Minor gaps remain (e.g., dry_run semantics), but overall it is complete for 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 provides an excellent example for the `mapping` parameter, clarifying its format and purpose. However, `dry_run` and `request_id` are not explained, and with 0% schema coverage, the description only partially compensates for the missing parameter documentation.

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: detect and repair broken references in visuals. It distinguishes two modes (diagnose vs. repair) and is specific about the resource and action, differentiating it from sibling tools like pbi_replace_visual_field.

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

Usage Guidelines5/5

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

The description explicitly explains when to use the tool: without `mapping` it only diagnoses, and with `mapping` it repairs after validating destinations. It also warns against guessing field mappings, providing clear guidance on responsible use.

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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