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Salesbot - B2B outreach LinkedIn MCP Server with CRM and email

import_linkedin_company_list

Idempotent

Import and persist companies from a LinkedIn company search URL or Sales Navigator account-search/list URL. This is for COMPANY/ACCOUNT results, not people. Creates or updates a durable Salesbot company list and returns prospect_company_id for every company. Optionally also creates company records in the CRM. After import, call list_companies, research each company with search_web, save the findings with update_company_research, then find current employees with search_linkedin_people and pass prospect_company_id to upsert_linkedin_contact.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax companies for this call (default 10; capped by the active LinkedIn safety preset).
cursorNoContinuation cursor returned by a previous import.
list_nameYesName of the durable company list in Salesbot.
search_urlYesFull LinkedIn company-search, Sales Navigator account-search, saved-search or account-list URL.
save_to_crmNoAlso create/update company records in CRM. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare non-read-only, idempotent, non-destructive, open-world behavior. The description adds substantive context beyond that: it creates or updates a durable list, returns prospect_company_id per company, and can optionally also write CRM records, plus the limit is capped by a safety preset.

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?

Front-loaded with the core action and scope exclusion, then the workflow chain. Slightly long, but nearly every clause carries routing or behavioral information rather than padding.

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?

No output schema exists, yet the description covers the return value (prospect_company_id), the persistence side effect, the optional CRM write, and the follow-up workflow. Nothing an agent needs to invoke it correctly 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?

Schema description coverage is 100%, so the schema already documents limit, cursor, list_name, search_url, and save_to_crm. The description reinforces the URL kinds and the CRM side effect but adds no syntax or format detail beyond the schema — the expected baseline.

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?

Specific verb+resource ('Import and persist companies') with explicit scope disambiguation: 'This is for COMPANY/ACCOUNT results, not people.' Also states the source URL types, which separates it from sibling search tools.

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?

Provides a complete when-to-use narrative and an explicit downstream sequence naming the sibling tools to call afterwards (list_companies, search_web, update_company_research, search_linkedin_people, upsert_linkedin_contact). No inference needed.

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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