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Calculate Pert

calculate_pert

Calculate PERT expected hours, standard deviation, variance, risk, and confidence intervals from optimistic, most likely, and pessimistic task estimates.

Instructions

Calcula horas PERT, desviación, varianza, riesgo y referencias de confianza.

Cada tarea necesita task_id, name, optimistic_hours, most_likely_hours y pessimistic_hours, con optimista <= más probable <= pesimista. Máximo 300 tareas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tasksYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose real behavior: the exact per-task shape, the ordering invariant optimistic <= most_likely <= pessimistic, and a hard cap of 300 tasks. It does not state whether anything is persisted or what the confidence references mean, so it falls short of a 5.

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?

Three short sentences, front-loaded with the purpose, then input requirements, then the limit. Every clause carries information and none repeats the name or title.

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?

An output schema exists, so return values need not be explained, and the inputs are documented despite an empty schema. The remaining gap is workflow positioning relative to the many sibling generation/estimation tools, which an agent would need in order to pick this one confidently.

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

Parameters4/5

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

Schema coverage is 0% and the item schema is an opaque object with additionalProperties, so the description is the only documentation of field names (task_id, name, optimistic_hours, most_likely_hours, pessimistic_hours) and their validity constraint. It adds substantial meaning, though it omits optionality and any per-field detail beyond the names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and a concrete set of outputs ('Calcula horas PERT, desviación, varianza, riesgo y referencias de confianza'), so the agent knows exactly what is computed. It does not, however, distinguish itself from nearby siblings such as build_risk_register or compare_scenarios, which also touch risk/scenario territory.

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

Usage Guidelines2/5

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

There is no when-to-use context: nothing says this is the estimation step that should follow wbs_breakdown or feed apply_buffers/build_risk_register. The only guidance given is an input constraint (max 300 tasks), which is a limit, not usage direction.

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