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GitHub + Linear Dev Workflows

GitHub + Linear Dev Workflows

by r28ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

linear_issue_get

Read-onlyIdempotent

Fetch a single issue by UUID or human identifier like ENG-123 to retrieve its description, comments, sub-issues, relations, and attachments.

Instructions

Get one issue with its description, comments, sub-issues, relations and attachments. Accepts either the issue's UUID or its human identifier, such as 'ENG-123'.

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": {
      "variables": "<variables>"
    },
    "name": "linear_issue_get"
  }
}

Input Schema

NameRequiredDescriptionDefault
variablesYesWhich issue to fetch.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so the safety profile is covered. The description adds real value beyond them by disclosing the return payload scope (comments, sub-issues, relations, attachments), which matters because there is no output schema. It stops short of noting permissions or whether the nested collections are paginated.

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?

Two short sentences, front-loaded with what is returned and followed by accepted input formats. Nothing is padded, though the second sentence duplicates schema text that the agent will already read.

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 single-read tool with a fully documented one-parameter schema and annotations covering safety, the description is nearly sufficient; it substitutes for the absent output schema by listing returned sections. Missing only edge-case behavior such as error handling for an unknown identifier or size/pagination of nested collections.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the description's identifier sentence ('UUID or ENG-123') restates the schema's own parameter description almost verbatim. Baseline 3 applies: the schema carries the semantics and the description adds no syntax, format or fallback detail beyond it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource ('Get one issue') and enumerates the payload it returns (description, comments, sub-issues, relations, attachments). The word 'one' implicitly separates it from linear_issues_list and linear_search_issues, but no sibling is named explicitly.

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?

Usage is only implied: fetching a single, already-identified issue. The description never states when to prefer this over linear_search_issues (discovery by text) or linear_issues_list (browsing), which is the most likely routing decision an agent faces here.

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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