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Flux LinkedIn MCP

Create Audience Segment

create_segment

Create a new audience segment (named group of people) for the authenticated user. Set type='manual' for a hand-curated list (use add_to_segment to populate it), type='smart' with a config predicate (e.g. {title_contains:'VP'}) to define a dynamic segment resolved on-demand by resolve_segment, or pass template to pre-fill config from a built-in template (type defaults to 'smart'). Free — costs 0 credits.

Input Schema

NameRequiredDescriptionDefault
nameNoUser-facing segment name. Optional when `template` is supplied — the template's default name is used.
typeNoSegment type. 'manual' = hand-curated person list. 'smart' = dynamic predicate (requires `config`). 'auto_stream' = system-managed from a stream. Defaults to 'smart' when `template` is provided.
configNoPredicate object for smart segments. Supported keys: title_contains, company_contains, company_industry, engaged_with_topics. Required when type='smart' and no template is supplied.
templateNoOptional pre-built template id. Available: 'vp_plus' (VP+ decision-makers), 'c_suite' (C-suite executives), 'heads_of' ('Head of' leaders), 'founders' (Founders & co-founders), 'fintech_leaders' (Fintech leaders), 'agency_owners' (Agency owners). Pre-fills `config` and defaults `type` to 'smart'. Explicit `config` overrides template values on collision.
descriptionNoOptional longer description
source_stream_idNoOptional stream that produced this segment (for traceability)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose valuable behavior: the operation is free (0 credits), smart segments are resolved on-demand rather than materialized, and template defaults type to 'smart'. However it says nothing about authentication/permission requirements, uniqueness or naming conflicts, or what happens on invalid config keys.

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?

A single dense sentence that front-loads the core action and then walks the mode decision in reading order, with zero filler. Length is justified by the branching creation modes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Six parameters, nested object, two enums, and no output schema — the description covers how to construct the segment but never says what is returned (e.g., segment id needed for a follow-up add_to_segment call) or how auto_stream/source_stream_id relate to the other modes. Adequate, with a clear gap for a create tool lacking an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds genuine meaning beyond the schema: a concrete config example ({title_contains:'VP'}), the defaults interaction between template and type, and the rule that explicit config overrides template values on collision.

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?

States a specific verb and resource ('Create a new audience segment') and immediately disambiguates the three creation modes (manual, smart, template). It also names the sibling tools that operate on the result (add_to_segment, resolve_segment), so an agent can distinguish it from related tools without opening a schema.

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

Usage Guidelines5/5

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

Explicitly routes the agent by mode: use type='manual' with add_to_segment to populate, type='smart' with a config predicate resolved by resolve_segment, or a template to pre-fill config. Each alternative is named alongside the condition that selects it.

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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