Skip to main content
Glama
velesnitski

yt-mcp

by velesnitski

get_issue

Retrieve full details of a YouTrack issue, including comments and custom fields, in markdown or JSON format.

Instructions

Get full details of a YouTrack issue.

Default returns a markdown render (chat-friendly). format="json" returns a normalized JSON dict — flat keys for id/summary/state/ assignee + assignee_login, a tags list, a custom_fields dict (custom field name → value), and a links list. Matches the JSON shape used by pulse and handoffs so consumers see one consistent structure across tools.

Use fields to override the field selector for power callers who need specific YT response fields. When fields is set and format="json", the raw YT response is returned unaltered (no normalization, full control). When fields is set and format="report", the markdown render may be sparse if the selector is narrower than the default.

Args: issue_id: Issue ID or URL. include_comments: Include comments in default selector (default: True). format: "report" (default markdown) or "json" (normalized dict). fields: Override the YT fields selector. Empty uses a richer default with login on assignee, presentation/text on custom-field values, and tags + created. instance: YouTrack instance (optional). include_bots: Also show workflow-automation nags and service stamps in comments (hidden by default; the hidden count is always reported). comment_chars: Per-comment truncation in compact mode (default: 200; 0 = untruncated).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNo
formatNoreport
instanceNo
issue_idYes
include_botsNo
comment_charsNo
include_commentsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses default markdown, normalized JSON, fields override behavior, comment bot filtering, and truncation. Thorough and honest.

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?

Well-structured and front-loaded with main purpose and formats. Arg list is clear. Could be slightly more concise, but every part contributes value.

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?

Given 7 parameters, no annotations, and presence of output schema, the description thoroughly covers behavior and usage. No gaps identified for agent selection and invocation.

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 description coverage is 0%, but description adds meaning to all 7 parameters: issue_id (ID or URL), format (report/json), fields (override with impact), include_comments, include_bots, comment_chars, instance. Greatly enhances schema.

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 'Get full details of a YouTrack issue' and explains the two output formats (markdown vs JSON), distinguishing it from sibling tools like search_issues or get_issues.

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?

Implicitly tells when to use: for full details. Explains default behavior and how to get JSON. Does not explicitly exclude other tools but provides clear context for decision.

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