ContentIn — LinkedIn Ghostwriter

Generate post ideas

generate_ideas

Generate fresh LinkedIn post ideas for a COLD request — the user wants options but has NOT given you material of their own ('what should I post about this week?', 'ideas for [topic]'). Ideas are grounded in the user's content pillars and their substance bank, so they are theirs rather than generic. Do NOT use this when the user has already given you their own material, a brief, or a concept in their words — that goes to write_post_in_my_voice, always. Each idea comes back with a hook and a briefing you can pass straight into write_post_in_my_voice.

Input Schema

TableJSON Schema

Name	Required	Description
`count`	No	How many ideas (1-10, default 5).
`style`	No	Optional. Omit to get a mix across all three styles, which is usually what the user wants.
`topic`	Yes	The topic or theme to generate ideas about.
`content_pillar`	No	Name of one of the user's content pillars that matches this topic. Omit if none fits.

TDQS

A4.5/5.0

Behavior4/5

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

The description adds behavioral detail beyond annotations: it explains that the output includes a hook and a briefing ready for write_post_in_my_voice, and that ideas are personalized to the user rather than generic. With annotations marking readOnlyHint=false, the generative nature is clear, but the description could mention any side effects (e.g., if ideas are saved).

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 efficient and front-loaded, with the core purpose in the first sentence. It covers use conditions, exclusion criteria, and output format without fluff. Minor improvement: could be slightly tighter by removing 'always' at the end.

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 the absence of an output schema, the description compensates well by describing what each idea contains (hook and briefing). It covers the cold request scenario thoroughly. Could mention that the agent should ensure the user's substance bank is available, but overall adequate for the tool's complexity.

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 coverage is 100%, so baseline is 3. The description adds value by explaining that the 'style' parameter should usually be omitted, the 'content_pillar' parameter should match the user's existing pillars, and the 'count' defaults to 5. This contextual guidance helps the agent set parameters optimally.

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 specifies the verb ('Generate'), the resource ('LinkedIn post ideas'), and the key condition ('for a COLD request'). It explicitly distinguishes this tool from the sibling tool write_post_in_my_voice, so the agent can differentiate when to use each.

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

Usage Guidelines5/5

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

The description includes explicit when-to-use ('user has NOT given you material') and when-not-to-use ('user has already given you their own material' – directs to write_post_in_my_voice). It also explains that ideas are grounded in the user's content pillars and substance bank, providing clear usage context.

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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TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: capturing user substance, generating ideas from scratch, writing posts from user material, repurposing posts, scheduling, publishing, listing, and analytics. There is no ambiguity or overlap, as even similar actions like publishing and scheduling are differentiated by timing and require separate confirmation flows.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., capture_substance, generate_ideas, write_post_in_my_voice). The verbs are descriptive and the nouns clearly indicate the resource or action, making the naming predictable and easy to understand.

Tool Count5/5

With 8 tools, the server is well-scoped for a LinkedIn ghostwriting assistant. Each tool covers a necessary step in the content lifecycle: capturing input, generating ideas, writing, repurposing, scheduling, publishing, listing, and analytics. The number is neither excessive nor insufficient for the domain.

Completeness4/5

The tool set covers the core workflow of content creation, scheduling, publishing, and analytics. Minor gaps exist, such as no direct post editing (beyond repurposing), no explicit pillar management, and no deletion tool. However, these are edge cases and do not hinder the primary ghostwriting purpose.

Resources

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