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LinkedIn posts of a profile or company

linkedin-posts
Read-only

LinkedIn posts API for AI agents: send one LinkedIn profile or company page URL and get its recent posts as JSON, each with text, date, likes, comments, shares, author, post URL and the reposter when it is a repost. Up to 20 posts per call; pay per post in stablecoins. A URL with no posts is not charged.

Input Schema

NameRequiredDescriptionDefault
urlYesLinkedIn profile URL (https://www.linkedin.com/in/<name>/) or company page URL (https://www.linkedin.com/company/<name>/); one URL per call
limitNonumber of posts wanted, 1 to 20 (default 5). A larger value is lowered to 20 and 0 or a negative one means the default; the quote follows the value used.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, non-idempotent and non-destructive, so the safety profile is covered. The description goes further, disclosing the 20-post cap, stablecoin payment per post, and that empty-result URLs are not charged, which are behavioral facts the annotations cannot express. It stops short of describing pagination or rate limits.

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?

Three tight sentences, front-loaded with purpose, followed by limit/cost and the empty-result billing caveat. Every sentence adds distinct value with no filler.

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?

With no output schema, a two-parameter tool and rich annotations, the description is sufficient: it explains the input contract, the result shape and the billed/unbilled cases. An agent has everything needed to call it correctly.

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 coverage is 100%, so the schema already documents both the URL format and the limit bounds/default/clamping. The description restates 'one URL' and 'up to 20 posts per call', adding reinforcement but no syntax or meaning beyond the schema. Baseline 3 applies.

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?

States a specific verb and resource ('get its recent posts' from a LinkedIn profile or company URL) and enumerates the returned fields, so an agent can tell it apart from the account-oriented siblings like linkedin-profile-email or linkedin-jobs. It never names or contrasts an actual sibling, which is what keeps it short of a 5.

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?

Usage is implied: supply one profile/company URL and receive its posts. There is no explicit when-to-use vs-when-not guidance and no mention of alternatives like linkedin-company for other data, so the routing burden stays on the agent.

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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