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brand_truth_create

Capture brand truths from natural-language text. Submit prose, and the extraction pipeline stores atomic facts and reports added/skipped with reasons.

Instructions

Capture brand truths from plain natural-language text. Describe facts/observations about the brand in ordinary prose — a sentence or short paragraph (≤8000 chars). Do NOT pre-classify, pre-format, or guess the taxonomy: PostKing's LLM extraction pipeline reads the text and decides which atomic 'brand truths' to persist and which to skip (duplicates, too vague, or previously rejected facts). Returns { addedCount, skippedCount, added, skipped }: 'added' are the truths that were stored; 'skipped' lists each rejected quote with a reason. Review both — the skipped reasons tell you what didn't make it in and why.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesFree-form natural-language description of facts/observations about the brand (≤8000 chars). Write it as plain prose — the extraction pipeline handles classification.
brandIdNoBrand ID (defaults to active brand)
Behavior5/5

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

With no annotations provided, the description carries full burden and excels. It discloses the LLM extraction pipeline behavior, criteria for skipping (duplicates, too vague, previously rejected), the return format { addedCount, skippedCount, added, skipped }, and instructs the user to review skipped reasons. This is rich behavioral 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 well-structured: it starts with the core purpose, then gives format constraints, explains the underlying pipeline, details the return value, and ends with actionable advice. Every sentence adds value and there is no fluff or redundancy.

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?

Despite having no annotations and no output schema, the description is remarkably complete. It explains the tool's behavior, input format, output structure, and practical guidance on interpreting results. The only unaddressed aspect (brandId default) is covered by the input schema, so the description is fully sufficient.

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% for both text and brandId, so baseline is 3. The description reinforces text usage (plain prose, ≤8000 chars) but doesn't add significant new meaning beyond the schema. brandId is not mentioned in description, but schema handles it.

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 the tool's purpose: 'Capture brand truths from plain natural-language text.' It uses a specific verb (capture/create) and resource (brand truths), and distinguishes itself from sibling tools like brand_truth_get, brand_truth_update, and brand_truth_list by focusing on extraction from prose.

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 gives clear context on how to use the tool: write plain natural-language prose, don't pre-classify or format, and the pipeline will handle extraction. It implicitly differentiates from update/get/list siblings by focusing on creation from free text, but it doesn't explicitly state exclusions or name alternative tools.

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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