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linear_createCustomer

Add a customer to Linear by specifying name, employee count, revenue, and other account details.

Instructions

Create a customer

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCustomer name
sizeNoCustomer employee or seat count
tierIdNoCustomer tier ID
domainsNoCustomer email domains
logoUrlNoCustomer logo URL
ownerIdNoOwner user ID
revenueNoAnnual customer revenue
statusIdNoCustomer status ID
externalIdsNoExternal system IDs
mainSourceIdNoPrimary external source ID
slackChannelIdNoLinked Slack channel ID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNo
urlNo
nameNo
sizeNo
tierNo
ownerNo
slugIdNo
statusNo
domainsNo
logoUrlNo
revenueNo
createdAtNo
updatedAtNo
archivedAtNo
externalIdsNo
mainSourceIdNo
slackChannelIdNo
approximateNeedCountNo
Behavior2/5

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

Annotations (all false) offer no behavioral cues. The description only states 'Create a customer', which implies mutation but provides no further details about side effects (e.g., audit logs, webhook triggers) or required permissions. It adds minimal value beyond the tool name.

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?

The description is extremely concise at three words, with no fluff. However, it lacks structured information such as typical usage scenarios. While brevity is positive, the minimalism sacrifices completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 11 parameters and an output schema, the description is insufficient. It does not explain the creation flow, return value, or any constraints (e.g., required fields beyond 'name'). The agent must rely entirely on schema and tool name for context.

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?

The input schema covers all 11 parameters with descriptions (100% coverage). The description does not add any extra meaning or clarify parameter relationships. Since schema coverage is high, the baseline score of 3 is appropriate.

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 'Create a customer' clearly identifies the verb (create) and the resource (customer). It distinguishes from sibling tools like linear_updateCustomer or linear_getCustomers. However, it does not elaborate on what 'customer' means in the Linear context, which could cause ambiguity.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives such as linear_createCustomerNeed or linear_createIssue. The description does not mention prerequisites, contexts, or exclusions, leaving the agent to infer usage.

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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