Install
claude mcp add --transport http talktohumans https://api.talktohumans.app/mcpWho it's for
It suits people already using remote MCP clients such as Claude, ChatGPT, or Cursor who want AI to search the connections, conversations, and messages inside their own LinkedIn account: sales and business development use it to find warm introductions and mutual contacts, recruiters revisit past conversations with candidates, and founders map out partnership openings worth raising. The common thread is letting AI do the homework inside your own account data. It improves the efficiency of finding people and reviewing history; it does not replace you in building the relationship. If you expect automated bulk outreach to strangers, no tool of the automated-send kind appears in the evidence, so that path is unverified.
Two hard prerequisites rule other people out: first, you need a TalkToHumans account on the Pro plan, plus the TalkToHumans browser extension installed and connected to your LinkedIn; second, the AI client signs in to TalkToHumans, not to LinkedIn. Anyone without a Pro account, unwilling to keep a LinkedIn-connected extension resident in their browser, or uncomfortable with a third-party service handling the messaging flow is not a fit. Whether a free tier exists, whether the Pro plan is paid, and exactly which permissions the extension requests are not stated in the evidence — unconfirmed information.
First try in 3 minutes
Before you start, verify the prerequisites: your TalkToHumans Pro account is usable; the browser extension is installed and connected to your LinkedIn; and you accept one sign-in action — the AI client signs in to TalkToHumans and does not sign in to your LinkedIn account (the specific authentication protocol is not stated by the vendor). These are officially stated conditions, not optional steps you may skip.
The recommended primary path is the command-line hookup given in the official documentation. Run in your terminal:
claude mcp add --transport http talktohumans https://api.talktohumans.app/mcp
Then follow your client's prompts to complete the TalkToHumans sign-in. For the first try, pick the retrieval-style conversation overview tool; by its name it only reads:
linkedin_list_conversations
The observable success signal: the client returns the conversation list from your LinkedIn account, which means the extension connection and the TalkToHumans sign-in work end to end. If you use a graphical client, the secondary path is to create a new connector in the custom connector settings, name it TalkToHumans, and paste https://api.talktohumans.app/mcp as the endpoint — the same remote service. Which fields the returned list contains is not listed in the evidence — unconfirmed information.
Use cases
Case one: review the past conversation before writing a follow-up direct message to an existing contact. Scenario: you are reaching back out to a candidate or client you spoke with before, but cannot recall where things last stood. Have the AI call linkedin_list_messages, review the message exchange with that contact, and then decide your follow-up wording. The result you can expect within the evidence: get the message content from the conversation and restore context. The value of this step is turning "where did we leave off" into citable context rather than a guess from memory. The evidence gives neither the tool's parameter names nor its return fields, so "how to target one specific conversation" is confirmed only up to the tool's existence; for actual usage, defer to the tool description inside your client.
Case two: have the AI draft a message you will not send immediately. Scenario: you want to reach a new acquaintance with safer wording. Have the AI call linkedin_write_message_draft to generate a message draft for this contact; the tool's input parameters are not given in the evidence. Expected result: a piece of draft text for you to review. In other words, treat it first as a writing assistant, not a sending channel. Draft quality depends on how much context you provide, a point the evidence is also silent on. Note: where drafts are stored, whether they are sent automatically, and whether they can be withdrawn are all unstated in the evidence — unconfirmed information. Until that is cleared up, treat it as an operation that writes real content, and check it word for word before deciding whether to use it.
Boundaries of use
Read/write nature: the list-style conversation and message tools are retrieval by their naming, while linkedin_write_message_draft generates message content. The evidence does not label read/write attributes tool by tool, nor give any undo path, so no tool here can be called reversible, and a retrieval call is not zero-risk either.
Credentials and data requirements: a TalkToHumans Pro account and the browser extension connected to your LinkedIn are required; the sign-in happens on the TalkToHumans side — the AI client does not sign in to your LinkedIn account, and the specific authentication protocol is not stated by the vendor. The visible scope of your account data, rate or quota limits, and the impact on LinkedIn's platform risk controls are all unmentioned in the evidence — all unconfirmed items. Search results reach your AI client through TalkToHumans's remote service, so this data path belongs in your judgment too; the third-party service's stability and data retention policies are likewise unmentioned.
Side effects and confirmation points: the AI client's sign-in faces TalkToHumans, not your LinkedIn account; the browser extension stays connected to your LinkedIn long-term; message-draft operations should be checked word for word by a human before anything is sent.
The repository readme carries marketing wording such as "safest"; that is the vendor's own claim and not this guide's safety conclusion. Whether to authorize, and how far, judge that against your own requirements for data boundaries.