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Horizun PBI MCP

by HorizunGroup

pbi_build_evm_page

Build a Power BI EVM page that validates required measures (PV, EV, AC, CPI, SPI) and warns if any are missing, preventing meaningless output.

Instructions

Pagina EVM (Earned Value Management).

Espera medidas del tipo PV, EV, AC, CPI y SPI; si no las reconoce, lo avisa en vez de generar una pagina que no significa nada.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoEVM
seedNo
dry_runNo
categoryNo
measuresYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral transparency. It discloses a key validation behavior: if the expected measures are not recognized, the tool warns instead of generating a meaningless page. This goes beyond the basic creation action and provides useful safety context, though it does not mention other behaviors like error handling, permissions, or output format.

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 extremely concise: two short sentences that immediately state the purpose, then explain the validation behavior. Every word earns its place, and the key information is front-loaded. There is no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters and no schema descriptions, a complete description would need to address all parameters or point to the output schema. The description effectively covers the core purpose and the key behavioral nuance, but it leaves name, seed, dry_run, and category unexplained. The output schema exists but does not help the agent understand parameter semantics, so the description is only minimally complete for a tool of this complexity.

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

Parameters2/5

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

The input schema has 0% description coverage for its 5 parameters, so the description must compensate. It adds meaning to the required 'measures' parameter by listing the expected types (PV, EV, AC, CPI, SPI), but it entirely ignores the other parameters (name, seed, dry_run, category). Thus, it only partially compensates for the lack of schema descriptions, leaving significant ambiguity for the agent.

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 purpose: building an EVM (Earned Value Management) page, and specifies the expected measure types (PV, EV, AC, CPI, SPI). The verb 'build' and resource 'EVM page' are specific and distinguish it from sibling page-building tools that target other types of pages.

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

Usage Guidelines3/5

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

The description implies when this tool should be used (when creating an EVM page with relevant measures) and mentions a validation behavior (warns if measures are unrecognized). However, it does not explicitly state when not to use it or mention alternatives like pbi_build_executive_page or pbi_build_dashboard, leaving some ambiguity for an AI agent choosing among similar tools.

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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