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Flux LinkedIn MCP

LinkedIn Profile Posts

linkedin_profile_posts

Fetch recent posts from a LinkedIn person profile. 5 credits per page of 50, up to 250 posts, charged only for pages actually fetched. Results are stored in the post library (source_type='external') and linked to a linkedin_profiles row so profile-brief can render research-only prospects.

Input Schema

NameRequiredDescriptionDefault
refreshNoBypass the cache and re-fetch from LinkedIn (5 credits per page of 50, charged only for pages actually fetched). Default false: a previously-fetched result is returned free, with how long ago it was retrieved.
max_postsNoMaximum posts to return (1-250). 5 credits per page of 50, charged only for pages actually fetched.
linkedin_urlYesLinkedIn profile URL (e.g. https://www.linkedin.com/in/username)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and performs well: it discloses credit costs, page-based charging, the 250-post cap, caching behavior via the refresh parameter, and the persistence side effect of storing results in the post library and linking them to a linkedin_profiles row.

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 with no waste: purpose first, cost second, storage/linking behavior third. Every sentence carries actionable information and the most important scoping detail is front-loaded.

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 3-parameter tool with no output schema and no annotations, the description is largely complete: it covers cost, limits, caching, and the important side effect of writing to the post library. It stops short of describing the returned post shape, but the storage side effect gives enough context 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 description coverage is 100%, so the schema already documents linkedin_url, max_posts, and refresh in detail. The description reinforces the 250-post cap and per-page credit model but adds no parameter semantics beyond what the schema provides, making 3 the correct baseline.

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 names a specific verb and resource: 'Fetch recent posts from a LinkedIn person profile.' This clearly separates it from company-oriented siblings like linkedin_company_posts by specifying 'person profile,' though it does not explicitly name alternatives.

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 description implies usage by tying results to the post library and profile-brief for 'research-only prospects,' but it never states when to choose this tool over linkedin_post_search, get_posts, or linkedin_profile_info. The context is helpful but not explicit routing guidance.

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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