Skip to main content
Glama
velesnitski

yt-mcp

by velesnitski

get_stuck_handoffs

Identifies tasks stuck in handoff states by checking state-change activities, not just comments. Groups stuck items by transition pattern to reveal which handoff is most often dropped.

Instructions

Tasks that crossed a team boundary and haven't moved since.

Distinct from get_handoff_snapshot (sorts by updated, polluted by comments) and track_cross_dept_journey (forensic per-issue deep-dive). This walks state-change activities specifically, so an item in Ready for test with fresh comments but no state change in 14 days IS surfaced as stuck.

Algorithm:

  1. Find issues currently in handoff-receiving states (qa, release, rework, dev) on the board.

  2. For each, fetch state-change activities.

  3. Find the latest state change. If it happened ≥ stuck_days ago AND that transition crossed a team-ownership role boundary (dev→qa, qa→release, etc.) → STUCK.

Output is grouped by transition pattern (Dev→QA stalls, QA→Release stalls, etc.) so you see which handoff most often gets dropped.

Args: board_name: Board name (partial match), ID, or URL. stuck_days: Days without a state change to count as stuck (default 4). lookback_days: Velocity-window framing in the header (default 30). limit: Max items shown per transition section (default 10). format: "report" (markdown) or "json" (structured payload). instance: YouTrack instance (optional).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
formatNoreport
instanceNo
board_nameYes
stuck_daysNo
lookback_daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are present, so the description carries full burden. It details the algorithm step-by-step, explains that it surfaces items with fresh comments but no state change, and describes output grouping. This provides thorough behavioral transparency.

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 well-structured: a concise one-liner, then differentiation from siblings, algorithm details, output grouping, and parameter list. It is front-loaded with key information and every sentence 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?

For a tool with algorithmic complexity (6 parameters, output grouping), the description is highly complete. It covers purpose, usage, behavior, parameters, and output format. The existence of an output schema (even if not shown) reduces burden, and the description adds all necessary context.

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?

Given 0% schema description coverage, the description compensates fully by listing all six parameters with meaningful descriptions (e.g., 'board_name: Board name (partial match), ID, or URL'). This adds essential context beyond the bare schema types and defaults.

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: 'Tasks that crossed a team boundary and haven't moved since.' It uses a specific verb-resource combination and immediately distinguishes itself from sibling tools get_handoff_snapshot and track_cross_dept_journey, making its purpose unmistakable.

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

Usage Guidelines5/5

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

The description explicitly tells when to use this tool versus alternatives by naming get_handoff_snapshot and track_cross_dept_journey and explaining their differences. It also outlines the algorithm and output grouping, providing clear context for appropriate use.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/velesnitski/yt-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server