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ContentIn — LinkedIn Ghostwriter

by io.contentin
OverviewDiscussionsAdmin

List people who engaged with the user's posts

list_leads
Read-onlyIdempotent

List the people who engaged with THIS user's own LinkedIn posts — their name, headline, LinkedIn profile, how they engaged, what they commented, which posts pulled them in, and ContentIn's ICP fit score. Use it to answer 'who is engaging with me', to find warm contacts, or to see which posts attract the right audience. REACTIONS ARE INCLUDED: a single like creates a lead, so a lead with no comments is completely normal and does not mean something is missing. ABOUT THE SCORE: icp_score is computed BY CONTENTIN, by comparing the person's LinkedIn headline against this user's stated ideal customer profile. It is an estimate from a headline, not verified data about who they are. classified: false with icp_score: null means ContentIn HAS NOT SCORED THIS LEAD YET — report it exactly that way. It does NOT mean the person is a poor fit; those are different claims and only one of them is supported. When icp_score_source is 'user_override' the number is the user's own labelling, not ContentIn's. Results are ordered by ContentIn's computed score when you sort by icp_score, so a lead the user has manually re-labelled keeps its computed position while reporting their number. Page with before/before_id; profiles routinely have thousands of leads.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoOrdering, always descending. Default 'last_interacted' (most recent engagement first).
limitNoHow many leads to return (1-100, default 50).
beforeNoPaging cursor: pass back the next_before value from the previous response, VERBATIM, together with before_id. Do not build this yourself, and do not reuse a cursor across a different sort or filter set.
searchNoFree-text match against the lead's name and headline.
statusNoFilter by the user's lead funnel state. Default is everything EXCEPT 'dismissed' — the user already said no to those, so ask for them explicitly if you really need them.
before_idNoPaging cursor: the next_before_id from the previous response. Always send it alongside before, otherwise leads sharing a score or a timestamp can be skipped.
min_icp_scoreNoOnly leads scoring at least this (0-100). Applied to the user's override where they set one, otherwise to ContentIn's score. Leads that have not been scored yet are excluded by this filter.
interacted_sinceNoISO-8601 date. Only leads who engaged on or after this.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only and idempotent behavior, but the description adds crucial nuances: a single like creates a lead, null icp_score means not yet scored rather than poor fit, user_override changes the reported number but not sort position, and pagination requires before_id alongside before. These go far beyond the annotations.

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?

The description is long but every sentence earns its place, with the core purpose front-loaded and high-risk caveats clearly labeled. It contains no filler or mere repetition of the schema.

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?

There is no output schema, but the description enumerates the returned fields and explains the semantic traps around icp_score, classification, and pagination. With all 8 optional parameters already documented in the schema, nothing an agent needs to call this tool correctly is missing.

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

Parameters5/5

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

Schema coverage is 100%, yet the description still adds operational meaning: before must be a verbatim next_before value, before_id prevents skips on ties, min_icp_score excludes unscored leads, and icp_score sort uses the computed score even when a user override exists. This directly prevents misuse.

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?

The description states exactly what the tool returns: people who engaged with 'THIS user's own LinkedIn posts', including name, headline, profile, engagement type, comments, source posts, and ICP fit score. It is easily distinguished from siblings like list_my_comments or get_post_analytics.

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?

It gives explicit use cases: answer 'who is engaging with me', find warm contacts, and see which posts attract the right audience. It does not explicitly name sibling tools as alternatives or state when not to use it, so it falls just short of a 5.

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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