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Maheidem

@maheidem/linkedin-mcp

by Maheidem

linkedin_create_optimized_post

Generate an optimized LinkedIn post tailored to your professional role, industry, and desired tone, with optional hashtags and engagement questions.

Instructions

✨ Generate and create an optimized LinkedIn post

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accessTokenYesLinkedIn access token
topicYesPost topic or theme
roleYesYour professional role
industryNoYour industry
toneNoPost tone
includeHashtagsNoInclude relevant hashtags
includeQuestionNoInclude engagement question
Behavior2/5

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

No annotations provided, so the description carries full burden. It states it generates and creates a post, but does not disclose key behaviors such as whether it posts immediately, requires authentication beyond accessToken, or has any destructive effects. The word 'create' implies writing, but no further detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one sentence with an emoji, which is concise but not particularly structured. It front-loads the core action but lacks any breakdown or additional context. It is acceptable but could be more informative without adding length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 7 parameters, no output schema, and no annotations, the description is insufficient. It does not explain what the tool returns (e.g., post URL or confirmation), how it uses the parameters, or any side effects. The complexity of generating and posting warrants a richer description.

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 the baseline is 3. The description adds no additional meaning beyond the schema; it merely restates the tool's purpose. Parameters are well-described in the schema itself, so the description does not enhance understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'generate and create' and the resource 'optimized LinkedIn post'. It distinguishes from the sibling 'linkedin_create_post' by implying optimization, but does not explicitly differentiate from 'linkedin_generate_optimized_content', which may cause confusion.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like 'linkedin_create_post' or 'linkedin_generate_optimized_content'. No exclusions or prerequisites mentioned, leaving the agent to guess the appropriate 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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