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QuassLabs API

sprint_health

Sprint/delivery health advisor. POST 1-24 sprints (committed/completed points, optional per-ticket estimate-vs-actual data) and get computed metrics each with a good/warn/bad health tag: velocity avg + trend + stability, commitment reliability, spillover rate, mid-sprint scope change, estimate accuracy (median + chronic-overrun pct), and WIP concentration per assignee. Plus an overall grade and a ranked what-to-fix-first focus list. Pure math + advisory, no LLM.

Input Schema

NameRequiredDescriptionDefault
sprintsYesOldest first: [{name, committed_points, completed_points, added_points?, spillover_points?, tickets?: [{key, estimate_hours, actual_hours, status, assignee}]}].
team_sizeNo
sprint_length_daysNoDefault 10.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are present, so the description carries the burden and does disclose key traits: the 1-24 sprint limit, that it is 'pure math + advisory, no LLM' (no side effects or external calls), and the shape of results including health tags and an overall grade. It omits auth/rate-limit/permission details and whether it persists anything, which keeps it below 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action and the input contract, and every clause maps to a real output or constraint. It is dense and slightly run-on, but no sentence is wasted.

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?

For a 3-param, nested-array tool with no output schema and no annotations, the description covers the input semantics and fully summarizes the return payload (metrics list, health tags, overall grade, ranked focus list). The only meaningful omission is guidance on team_size and a note on how many sprints are needed for trend/stability to be meaningful.

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 67%; the description adds meaning by explaining that the array holds committed/completed points plus optional per-ticket estimate-vs-actual data and by tying estimate-accuracy output to that optional ticket data. team_size is still undocumented in both description and schema, leaving a gap.

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+resource ('POST 1-24 sprints ... get computed metrics') and enumerates exactly what is returned (velocity, commitment reliability, spillover, scope change, estimate accuracy, WIP concentration, grade, focus list). An agent can distinguish this aggregate health-advisory tool from point-focused siblings like estimate_sanity or commit_hygiene without opening the schema.

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 conveys the input contract and output shape but never says when to prefer this tool over related siblings (estimate_sanity, commit_hygiene, coverage_check) nor states exclusions. Usage is implied by the 'sprint/delivery health advisor' framing rather than explicitly routed.

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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