forecast-mcp

PERT three-point estimate (analytical)

pert_estimate

Read-onlyIdempotent

Compute a PERT estimate from optimistic, most-likely, and pessimistic task values to get expected durations, project mean, and values at chosen confidence levels.

Instructions

Compute a classic PERT estimate from tasks with optimistic / most-likely / pessimistic values. Returns each task's expected value and standard deviation, the rolled-up project mean and standard deviation, and the value achievable at each confidence level (via a normal approximation). Instant and deterministic - use it for a quick estimate or to cross-check a Monte Carlo forecast. Assumes tasks are independent and summed.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "tasks": "<tasks>"
    },
    "name": "pert_estimate"
  }
}

Input Schema

Name	Required	Description	Default
`unit`	No	Unit label for display.	days
`tasks`	Yes
`confidence`	No	Confidence levels (percent) at which to report achievable values.

Output Schema

Name	Required	Description	Default
`unit`	Yes
`items`	Yes
`projectMean`	Yes
`projectStdDev`	Yes
`projectVariance`	Yes
`confidenceLevels`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 27, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Adds substantial context beyond the readOnly/idempotent annotations: it is 'instant and deterministic', uses a 'normal approximation', and assumes 'tasks are independent and summed'. These methodology and assumption disclosures materially shape how an agent should trust and apply the result.

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 tight sentences with zero waste: capability, outputs, and usage context are front-loaded, followed by the key independence/sum assumption. Every clause earns its place.

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?

An output schema exists so return values need not be detailed, yet the description still summarizes them usefully. Combined with the stated assumptions and determinism, an agent has everything needed to invoke and interpret this tool correctly.

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?

The description clarifies that tasks carry optimistic/most-likely/pessimistic values and that confidence levels produce 'value achievable at each confidence level', adding meaning beyond the schema. With 67% schema coverage the 'unit' parameter is not elaborated, so it is slightly short of complete.

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?

States a specific verb and resource ('Compute a classic PERT estimate') and names the exact inputs (optimistic/most-likely/pessimistic). It also contrasts itself against the Monte Carlo forecast sibling and the analytical approach, so an agent can distinguish it from the forecast_* family.

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?

Explicitly says to 'use it for a quick estimate or to cross-check a Monte Carlo forecast', giving a clear selection context against the alternative predictive tool. It stops short of naming the sibling tools directly or stating when-not to use it, so it falls just under full marks.

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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