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by OnStartups

linkedin_sentiment_render_report_html

Render HTML sentiment reports from LinkedIn post data with sentiment badges, engagement stats, and author cards using Jinja2 templates—no LLM needed for fast deterministic output.

Instructions

Renders a polished HTML sentiment report from LinkedIn post data. No LLM required — uses Jinja2 templates for fast, deterministic rendering with sentiment badges, engagement stats, and author cards.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
posts_jsonYesJSON array of LinkedIn post objects (from the Search LinkedIn Posts action output), or a variable reference like {{linkedin_posts}}.{{linkedin_posts}}
sentimentsNoOptional JSON object mapping postId to sentiment label (e.g. {"postId123": "Positive"}). If omitted, sentiment is auto-classified from post content.
report_titleNoTitle displayed at the top of the report.LinkedIn Post Sentiment Analysis
output_variable_nameYesVariable name to store the rendered HTML report.sentiment_report_html
Behavior4/5

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

With no annotations, description well discloses it uses Jinja2 templates for deterministic rendering, and lists included report components. Adds context beyond schema about performance and content.

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?

One concise sentence that effectively conveys the tool's action and key attributes without unnecessary detail.

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?

Given no output schema, description adequately explains what the report contains. Could mention return format (string of HTML) but is sufficient for a rendering tool.

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 coverage is 100%, so baseline is 3. Description does not elaborate on parameters beyond what schema already provides.

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?

Description clearly states it renders an HTML sentiment report from LinkedIn post data, and lists specific components (sentiment badges, engagement stats, author cards). This distinguishes it from sibling render tools that specialize in other domains.

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?

Implied usage from description (for LinkedIn post reports, no LLM needed), but no explicit guidance on when to use this vs other render tools or when not to use it.

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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