Skip to main content
Glama
HorizunGroup

Horizun PBI MCP

Official
by HorizunGroup
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Local

pbi_list_tables

Read-only

List Power BI tables with columns, types, visibility, and row counts. Use summary mode for an overview, then request details for specific tables from live models or .pbip files.

Instructions

Lista tablas con columnas, tipos, visibilidad y conteos.

source: 'live' (modelo abierto, por defecto) o 'pbip' (archivos TMDL).

Empieza por detail='summary'. Devuelve nombre, visibilidad y recuentos, sin la lista de columnas. Con detail='full' (por defecto, por compatibilidad) un modelo de siete tablas ocupa ~28.000 caracteres y uno corporativo puede llenar buena parte de la ventana de contexto en una sola llamada.

tables: acota a esas tablas por nombre. Es lo que se usa despues del resumen para pedir el detalle solo de las que interesan. Un nombre que no existe falla y devuelve los disponibles, en vez de una lista vacia.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailNofull
sourceNolive
tablesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.2.0
    • addedInput schema / properties / detail
      Added value: +{
+  "default": "full",
+  "title": "Detail",
+  "type": "string"
+}
    • addedInput schema / properties / tables
      Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Tables"
+}
  2. First observedv1.0.1

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses important behaviors: the full detail output can be extremely large (~28,000 chars for a small model), invalid table names cause an error that returns available tables, and the `source` parameter selects between live and pbip. This adds substantial value beyond structured 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 dense but every sentence adds value: purpose, parameter semantics, usage advice, and a warning about output size. It is well-structured with bold emphasis on the critical advice, making it easy to scan.

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?

With an output schema present and annotations provided, the description covers all necessary aspects: what is returned, how to control output size, and error behavior. The tool is simple enough that this description is fully sufficient for an agent to select and invoke 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 schema has 0% description coverage, but the description thoroughly explains all three parameters: `source` (live vs pbip), `detail` (summary vs full with size implications), and `tables` (filtering and failure behavior). It fully compensates for the schema's lack of descriptions.

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 states 'Lista tablas con columnas, tipos, visibilidad y conteos' — a specific verb and resource, clearly distinguishing it from sibling tools that list measures, relationships, roles, etc. The title and description align perfectly, leaving no ambiguity about what the tool does.

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 gives clear usage guidance: start with `detail='summary'` to avoid context overload, then use `tables` to request details for specific tables. It does not explicitly name alternatives or exclusions, but the context and parameter guidance are highly actionable.

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

Install Server

Other Tools