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southleft

LinkedIn Intelligence MCP Server

by southleft

get_my_posts

Retrieve your LinkedIn posts with metadata and URNs for analytics, enabling content review and performance tracking.

Instructions

Get your own LinkedIn posts.

Uses the data provider to retrieve your authored posts with metadata. Returns post URNs that can be used with get_my_post_analytics.

Args: count: Number of posts to retrieve (max 100)

Returns list of your posts with URNs, content, and timestamps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that it 'retrieves' posts (implying read-only), mentions the data provider context, and specifies a maximum count constraint. However, it doesn't address permissions, rate limits, pagination, or error behaviors that would be important for a retrieval tool.

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?

The description is efficiently structured with a clear purpose statement, implementation context, usage guidance, parameter explanation, and return value description in just four sentences. Every sentence adds value with zero redundancy.

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 tool has an output schema (so return values don't need description), one simple parameter, and no annotations, the description provides good coverage of purpose, usage context, parameter meaning, and constraints. It could be more complete by addressing authentication or error handling, but it's reasonably comprehensive for this complexity level.

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?

The description adds meaningful context for the single parameter 'count' by explaining it's the 'Number of posts to retrieve' and specifying 'max 100'. With 0% schema description coverage and only one parameter, this provides good semantic clarification beyond the basic integer type in the schema.

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 specific action ('Get your own LinkedIn posts'), the resource ('your authored posts'), and distinguishes it from siblings by specifying it's for your own posts only (unlike get_profile_posts which could be for others). It uses a precise verb and resource combination.

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 clear context about when to use this tool ('Get your own LinkedIn posts') and mentions a specific follow-up use case ('Returns post URNs that can be used with get_my_post_analytics'). However, it doesn't explicitly state when NOT to use it or name alternative tools for similar purposes.

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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