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Flux LinkedIn MCP

Preview a LinkedIn profile

preview_profile

Analyze a public LinkedIn profile or company page without a Flux account: pulls the latest ~50 posts (once per profile) and returns a preview id, post count and a one-line posting summary. Use preview_posting_insights with the preview_id for the full breakdown, and claim_preview after the person signs in to make it their own dashboard.

Input Schema

NameRequiredDescriptionDefault
linkedin_urlYesPublic LinkedIn profile or company URL, e.g. https://www.linkedin.com/in/janedoe

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description does real work: it discloses that it pulls the latest ~50 posts, that this happens once per profile, and what the response contains (preview id, post count, one-line summary). Auth context is also covered via the Flux-account and sign-in claim flow, though error or rate-limit behavior is not addressed.

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 the core action and scope, followed by the return values and then the next-step tooling. Every sentence carries actionable information with no filler.

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 one parameter, no output schema, and no annotations, the description explains the key return values and the follow-on workflow sufficiently for an agent to call it correctly. It could be slightly richer about what the 'one-line posting summary' contains or how errors are surfaced, but nothing essential is missing.

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?

There is one parameter with 100% schema description coverage, and the schema already defines linkedin_url with an example. The description implies the input is a public LinkedIn profile or company URL but adds no syntax or format detail beyond the schema, so baseline 3 is appropriate.

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 (analyze) and resource (a public LinkedIn profile or company page) and specifies the account-free scope, which helps separate it from account-based tools. It does not explicitly differentiate from siblings like linkedin_profile_info or get_profile_summary, so it falls just 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 Guidelines4/5

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

Clearly says when to use it: public profiles/company pages without a Flux account, with the 'once per profile' limit noted. It also names the follow-on tools preview_posting_insights and claim_preview with their conditions, though it does not explicitly say when not to use it in favor of sibling tools.

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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