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linkedin_post

Write authentic LinkedIn posts about project wins, lessons, or insights to attract inbound leads. Generates a post with hook, story, takeaway, and soft CTA.

Instructions

Write a concise, authentic LinkedIn post about a project win, lesson learned, or professional insight. LinkedIn is where freelancers get inbound leads — but most avoid posting because writing feels awkward. This generates a post in a natural professional voice (150–250 words): specific hook, the story, the takeaway, a soft CTA. Does not count against your monthly draft limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesWhat to post about (e.g. 'won a new e-commerce client', 'a mistake I made on a project', 'why I stopped working with certain clients', 'delivered a rebrand under budget')
key_insightYesThe one insight or takeaway you want readers to leave with
your_roleNoOptional: your professional role/title to anchor the post (e.g. 'freelance web designer', 'brand consultant', 'UX contractor'). Default: freelancer.
include_ctaNoOptional: include a soft call-to-action at the end (e.g. follow for more, DM for work). Default: true.
toneNoOptional: post tone. 'professional' = polished, 'conversational' = warm and relatable, 'direct' = no filler, just the point. Default: conversational.
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the post is concise, authentic, 150–250 words, includes hook/story/takeaway/CTA, and does not count against draft limits. No destructive behavior, but transparent about generation characteristics.

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 two sentences with additional detail about word count and structure. It is front-loaded with purpose and context, concise without being overly terse. Minor room for tightening.

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 and no annotations, the description explains the output format (150–250 word post with specific structure) and usage context. It does not explicitly state return type but is adequate for a generation 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 description coverage is 100%, so baseline is 3. The description does not add new parameter details beyond the schema, but it provides context for the tool's purpose. The schema already explains each parameter adequately.

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 writes a LinkedIn post, specifies the content types (project win, lesson learned, professional insight), and distinguishes it from sibling tools (none other for LinkedIn posts). It also mentions it does not count against monthly draft limit, adding clarity.

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 context on when to use the tool (for LinkedIn posts about professional insights) and why (to get inbound leads), but does not explicitly mention when not to use it or compare to alternative tools. However, the context is clear enough for appropriate use.

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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