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knowledge_create

Create a branded knowledge-base item for text or JSON content. Submit content, and PostKing auto-generates description/tags if omitted, returning an operation ID to poll for completion.

Instructions

Async. Create a new knowledge-base item for the brand. Returns { operationId, status } — poll get_job until state=completed. If description or tags are omitted, PostKing auto-generates them from the content. For contentType='json', content must be valid JSON — this tool validates client-side before calling the API. Typically takes ~15–30 s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesHuman-readable name for the knowledge item (e.g. 'Brand Voice Guidelines').
tagsNoOptional tags for filtering (e.g. ['brand', 'voice']). Auto-generated if omitted.
brandIdNoBrand ID (defaults to active brand)
contentYesThe full content body. Must be valid JSON string when contentType='json'.
isGlobalNoMark as account-wide knowledge (shared across all the user's brands)
contentTypeYesContent format: 'text' for prose/markdown, 'json' for structured data.
descriptionNoOptional short description. If omitted, PostKing auto-generates one from the content.
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states async execution, the return format { operationId, status }, the need to poll get_job, auto-generation of description/tags when omitted, client-side JSON validation, and the typical 15-30s duration. This is exemplary 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 three sentences long, each carrying critical information: async nature, purpose, return/polling, and edge cases. It is front-loaded with 'Async.' to set expectations and contains no redundant wording.

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?

The description covers the return format, how to determine completion, validation behavior, timing, and optional parameter side effects. Although there is no output schema, the description provides sufficient guidance for an agent to invoke and handle the tool correctly.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining auto-generation behavior for omitted description/tags and validation requirements for contentType='json'. This enriches understanding of how parameters work in practice.

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 states a specific verb+resource: 'Create a new knowledge-base item for the brand.' It clearly distinguishes from sibling tools like knowledge_list, knowledge_get, knowledge_update, and knowledge_delete by focusing on the create operation.

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?

The description provides clear context for when to use this tool (creating a knowledge item) and includes important workflow details (async, poll get_job until completed). It does not explicitly mention alternative tools, but the sibling names and CRUD context make the usage obvious.

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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