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mdnaimul22

LinkedIn MCP Pro Max

by mdnaimul22

create_linkedin_post

Generate and publish a LinkedIn post from a topic. AI writes the post, optionally adds a call-to-action and image, then posts to LinkedIn.

Instructions

Generate and publish a new LinkedIn post using an internal AI writer.

The AI will craft a complete, publish-ready post based on the topic you provide. Optionally, a second AI pass generates a detailed visual prompt which is sent to the image generation engine to create and attach a professional image to the post.

Full pipeline (when include_image=True): 1. LLM writes the post text (topic + tone + optional CTA). 2. LLM writes a rich, detailed image generation prompt. 3. Image generator (Flux / Gemini) creates the image. 4. Browser uploads image + posts text together on LinkedIn.

Args: topic: What the post should be about (e.g., 'why clean code matters', 'lessons from 6 months of remote work'). tone: Writing style — 'professional' (default), 'storytelling', or 'thought-leader'. include_cta: If True (default), end with a question or call-to-action. include_image: If True, generate and attach an AI image to the post. Requires IMAGE_GEN_API_BASE to be configured. If image generation fails, the post is published text-only.

Returns: JSON string with: - status: 'success' or 'error' - generated_post: The complete text that was published. - character_count: Length of the published post. - topic: Echo of the original topic for traceability. - image_prompt: (if include_image) The prompt used for image gen. - image_url: (if include_image) Local path of the generated image. - image_warning: (if include_image failed) Reason why image was skipped. - message: Human-readable confirmation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toneNoprofessional
topicYes
include_ctaNo
include_imageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are provided, but the description discloses the multi-step pipeline, including the AI writer, image generation, and browser upload. It also notes configuration requirements (IMAGE_GEN_API_BASE), fallback behavior if image generation fails (publish text-only), and the return JSON structure, providing comprehensive behavioral context beyond the tool's basic function.

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 lengthy but well-structured with sections for pipeline, arguments, and returns. It is front-loaded with the core purpose and every sentence adds necessary detail, though some redundancy exists between the pipeline list and arg explanations.

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?

The description covers all aspects needed for correct invocation: inputs, defaults, side effects, error handling, configuration prerequisites, and return schema. The presence of a specified return JSON structure makes the tool self-contained despite missing formal output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero property descriptions, yet the description explains each parameter: topic with example, tone with specific style options, include_cta with its effect, and include_image with the conditional requirement and fallback. This fully compensates for the schema gap.

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 'Generate and publish a new LinkedIn post using an internal AI writer' – a specific verb and resource. It differentiates from the sibling 'interact_with_post' by focusing on creating new content rather than engaging with existing posts.

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?

It describes the full workflow and options, but does not explicitly state when to choose this over 'interact_with_post' or other content tools. The purpose is clear enough that usage context is implied, but no explicit alternatives or exclusions are given.

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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