harvest-forecast-mcp

team_utilization

Calculate team utilization by comparing scheduled hours from Forecast to logged hours from Harvest. See percentage utilization per person.

Instructions

Get team utilization: scheduled hours (Forecast) vs logged hours (Harvest) per person.

For each Forecast person with a linked Harvest user, shows their total
scheduled allocation and total logged hours in the date range, with a
utilization percentage.

Args:
    start_date: Start of date range (YYYY-MM-DD).
    end_date: End of date range (YYYY-MM-DD).

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": {
      "start_date": "<start_date>",
      "end_date": "<end_date>"
    },
    "name": "team_utilization"
  }
}

Input Schema

Name	Required	Description	Default
`end_date`	Yes
`start_date`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 11, 2026

TDQS

A4.4/5.0

Behavior4/5

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

No annotations exist, so description carries full burden. It discloses that it returns scheduled allocation, logged hours, and utilization percentage for persons with linked Harvest users. This is sufficient for a read-only reporting tool, though it doesn't mention potential limitations like missing linkages.

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?

One-line summary followed by a brief explanatory paragraph and explicit Args section. Every sentence adds value, no redundancy, and the most important info is front-loaded.

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 2 required parameters and an output schema available, the description explains the output concept clearly. No gaps for a simple reporting tool.

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?

Schema coverage is 0%, but description adds format (YYYY-MM-DD) and purpose for both 'start_date' and 'end_date', fully compensating for the schema's lack of detail.

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?

Description clearly states 'Get team utilization: scheduled hours (Forecast) vs logged hours (Harvest) per person.' It specifies the verb, resource, and scope, and distinguishes from sibling tools like time_summary or get future_scheduled_hours by focusing on utilization.

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?

Description implies usage when needing per-person utilization across a date range, but lacks explicit when-not-to-use or alternatives. Among 30+ siblings, no direct alternative is mentioned, so guidance is implied but not explicit.

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