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JackJProsp

Prosp MCP Server

by JackJProsp

Import From Post

import_from_post

Import LinkedIn post likers and commenters into a list, with future reactions included, to capture high-intent leads for outreach.

Instructions

Import the people who engaged with a LinkedIn post into a list.

This is the highest-intent cold source available. Someone who commented on a post about the exact problem you solve has done something; someone who matches a search filter has not.

include_future_reactions keeps the import open, so engagement arriving days later is pulled in without anyone touching it. Leave it on for a post that is still travelling.

Importing costs nothing. The capped resource is sending, so import broad and filter at the send step rather than the import step.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
list_idYes
post_urlYes
include_likersNo
include_commentersNo
include_future_reactionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
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 it does disclose real behavior: importing is free, sending is the capped resource, and include_future_reactions leaves the import open so later engagement is pulled in automatically. It omits other behaviors an agent would want, such as duplicate handling against existing list members, permissions, and whether the import is synchronous.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Opens with the purpose, then layers the differentiator, the parameter rule, and the cost model in short scannable paragraphs. The 'highest-intent cold source' framing is persuasive rather than operational, but it is brief and does support tool selection.

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?

An output schema exists, so return values need not be described, and the description plus schema together cover the core call. Gaps remain around how the import interacts with existing list contents (append vs replace) and required permissions for a 5-parameter write tool with no annotations.

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 0% across 5 parameters, so the description must compensate. It explains the one genuinely ambiguous parameter well (include_future_reactions keeps the import open for later engagement), but list_id, post_url, include_likers and include_commenters are never addressed.

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 (Import), a specific resource (people who engaged with a LinkedIn post) and a destination (into a list). It also draws a conceptual line against filter-based sourcing ('someone who matches a search filter has not'), which is exactly how a sibling like import_from_search is distinguished.

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?

Gives clear when-to-use framing ('highest-intent cold source') plus a conditional rule for include_future_reactions ('Leave it on for a post that is still travelling') and a strategic rule ('import broad and filter at the send step'). It never names the alternative tool to call instead, so it stops short of explicit when-not/alternatives routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.