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Flux LinkedIn MCP

LinkedIn People Search

linkedin_people_search

Search LinkedIn people. Filter by company, title, location, or free-text keywords. Costs 5 credits per call. Returns a single page of matching profiles.

Input Schema

NameRequiredDescriptionDefault
titleNoCurrent title filter (e.g. 'Director')
companyNoCurrent company name or LinkedIn slug
refreshNoBypass the cache and re-run the search live (costs 5 credits). Default false: a previously-fetched result is returned free, with how long ago it was retrieved.
keywordsNoFree-text search (e.g. 'data engineer fintech')
locationNoLocation filter (e.g. 'New York')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full disclosure burden. To its credit it surfaces the two most consequential traits: a cost of 5 credits per call and page-bounded results ('single page of matching profiles'). It says nothing about caching behavior (left to the refresh param schema), rate limits, authentication, or how to page beyond the first result set.

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 short sentences, zero filler, with the core action front-loaded and cost surfaced early. Every sentence contributes distinct information.

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?

All five params are optional and there is no output schema or annotations, so the description needs to stand on its own. It covers cost, result shape, and filters adequately, but omits that zero filters still incurs 5 credits and gives no hint about result volume or how to continue past page one.

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 every parameter including the non-obvious refresh cache tradeoff. The description only restates the filter names already present in the schema and adds no syntax, matching-behavior, or multi-filter combination guidance beyond it.

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 ('Search LinkedIn people') and enumerates the available filter dimensions, which naturally separates it from siblings like linkedin_company_search, linkedin_job_search, and linkedin_post_search. It never names an alternative explicitly, so the differentiation is implied by the resource noun rather than stated.

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 lists the filter axes (company, title, location, keywords) which implies how the tool is used, but gives no when-to-use guidance, no prerequisites, and no exclusions. Nothing tells the agent when to prefer linkedin_profile_info or traverse_graph over this search.

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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