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create_issue

Create a Linear issue with team, title, assignee, state, priority, labels, and description. Returns the new issue identifier.

Instructions

Create a Linear issue. Returns the created issue including its new identifier. team: team key or name, e.g. "GOV". assignee: user display name or email. See list_users. state: workflow state name, e.g. "Backlog". See list_states. priority: 0 none, 1 urgent, 2 high, 3 medium, 4 low. labels: label names on that team. See list_labels.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
teamYes
stateNo
titleYes
labelsNo
assigneeNo
priorityNo
descriptionNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden. It states the mutation ('Create') and the return behavior ('Returns the created issue including its new identifier'), which is valuable. It does not discuss failure modes, permissions, or side effects, but the core behavior is transparent enough for an agent to know what invoking it will do.

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 compact and front-loads the purpose and return value, then uses a clean parameter-by-parameter list. Every line adds information beyond the schema rather than repeating it.

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 7-parameter create operation with no annotations and no output schema, the description covers the action, the return value, and the semantics of all non-obvious parameters, and tells the agent which sibling tools to use for valid values. This is sufficient for correct 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%, so the description must explain parameters on its own. It does so for team, assignee, state, priority, and labels, including formats, examples, and references to sibling tools for valid values. The remaining params, title and description, are self-evident from their names and 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 opens with 'Create a Linear issue', a specific verb and resource, and adds what is returned. This clearly distinguishes it from sibling tools like update_issue or get_issue.

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?

It gives clear context for when to use the tool: to create a new Linear issue. It also points to list_users, list_states, and list_labels as lookup mechanisms for valid assignee, state, and label values. It does not explicitly say when not to use it versus update_issue, but the verb 'Create' makes this largely inferable.

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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