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kaistenberg

MCP Server for LinkedIn

by kaistenberg

Get Company Posts

get_company_posts
Read-only

Retrieve recent posts from any company's LinkedIn feed by entering the company name. Monitor their updates, announcements, and shared content.

Instructions

Get recent posts from a company's LinkedIn feed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_nameYesLinkedIn company name (e.g., "docker", "anthropic", "microsoft")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds the 'recent' qualifier and company focus, but doesn't disclose behaviors like time window limitation or pagination. This is consistent with annotations and adds modest context beyond them.

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 a single, well-structured sentence that front-loads the verb and resource. It contains no filler or redundant information, earning a top score for efficiency.

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?

For a simple read-only tool with one parameter and an output schema, the description is largely complete. It adequately communicates what the tool returns (company feed posts) and integrates with annotations. The only minor gap is the unspecified meaning of 'recent,' but this does not undermine overall completeness.

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?

The input schema has 100% coverage—company_name is described with examples. The tool description offers no additional parameter detail beyond what the schema provides, so it meets the baseline but adds no extra semantic value.

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 uses a specific verb ('Get'), identifies the resource ('recent posts from a company's LinkedIn feed'), and clearly differentiates from sibling tools like get_feed (general feed) and search_posts (search-based retrieval). This is unambiguous and contextually distinct.

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

Usage Guidelines3/5

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

The tool's purpose implies usage when company-specific posts are needed, but no explicit guidelines are given about when to prefer this over search_posts or get_feed. The description relies on inferred context from the name rather than stating exclusions or alternatives.

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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