✨ Use with your AI tools

TalkToHumans connects compatible AI clients such as Claude, ChatGPT, Codex, and Cursor to your LinkedIn workspace through MCP. Use your AI tools to search your network, prepare drafts for review, and turn team activity into useful reports.

Connect the remote server first. Follow the MCP setup guide, then start a new chat with the TalkToHumans tools enabled.

Find contacts

Your next useful conversation may already be in your LinkedIn network. Describe the person you want. Your AI client can turn that into structured searches across the contacts you have synced or imported, rank the results, and save the shortlist as a TalkToHumans view.

What the MCP can search

The linkedin_list_contacts tool can search across: → name and LinkedIn URL → headline, role, about text, and experience → current or previous company → location → company size → TalkToHumans tags and notes → connected or unconnected relationship state → pending invitation state → contacts inside a specific conversation Your AI client can combine several searches, inspect the results, and keep paging when it needs a wider sample.

Find leads

Give the AI a real ICP and tell it how to rank the results.

Use TalkToHumans MCP to find 20 people in my LinkedIn network who match this ICP:

[Describe the company, role, size, geography, problem, and buying signal.]

Return a table with:
- name
- current role and company
- LinkedIn URL
- why the person fits
- relationship context
- a 1 to 5 fit score
- one useful outreach angle

Do not draft messages yet. Let me review the shortlist first.

The last line matters. Research first. Outreach after you agree with the list.

Find hires

Search my TalkToHumans contacts for potential hires for this role:

[Paste the role, required experience, location, and useful background.]

Prioritize people I am already connected to or have spoken with before.
Explain the evidence for each match and flag missing information.

This works well for warm sourcing, past candidates, former colleagues, and people whose job changed since you last spoke.

Find partners and warm paths

Search my LinkedIn network for people who could help me reach this audience:

[Describe the audience.]

Look for agencies, consultants, creators, investors, recruiters, operators,
past customers, and people working with the companies I care about.

Separate the results into:
1. direct prospects
2. potential partners or referrers
3. people who may provide a warm introduction

The strongest result is often someone who knows the buyer, not the buyer themselves.

Save the shortlist in TalkToHumans

Once the list looks right, ask:

Create a TalkToHumans contact view called "[View name]" for the approved contacts.
Add a short description explaining why this list exists.

The MCP uses linkedin_create_contact_view to create a focused list. Open it in TalkToHumans, review each person in context, and decide who deserves a message.

Keep the judgment human

AI is good at scanning profile data, finding patterns, and suggesting angles. It does not know every history, political detail, or reason to leave someone alone. Use it to narrow the network. You still choose the people and the message.

Draft outreach

Giving AI a LinkedIn send button would be fast and, honestly, a terrible idea. The MCP handles the useful preparation without sending anything. Give your AI client a set of profile URLs and the reason you want to contact them. It can import the contacts, organize them, add context, and save a draft for each person. You open TalkToHumans to review and act. That boundary is deliberate.

What the workflow can do

The TalkToHumans MCP can: → list the LinkedIn accounts you can access → import one contact from a linkedin.com/in/ profile URL → enrich a contact and their primary company → add or remove tags → save account-scoped notes → create a contact view for the group → save a message draft for a contact or existing conversation The draft tool returns a TalkToHumans link and confirms that nothing was sent. The MCP does not currently apply a sequence or bulk-send the prepared messages. Use the app for that final step.

Import a list and prepare drafts

Use TalkToHumans MCP for the LinkedIn profile URLs below.

Goal: [Explain the project and why these people were selected.]
Account: [Name the LinkedIn account if the workspace has more than one.]

For each valid profile:
1. import the contact
2. add the tag "[Tag name]"
3. add a note with the source and reason for outreach
4. draft a short, personal connection note or first message

Then create a TalkToHumans contact view called "[View name]".

Rules for the drafts:
- use only facts present in the supplied context or contact profile
- keep the tone low pressure
- do not invent familiarity
- do not send anything
- return the TalkToHumans review link for every contact

Profile URLs:
[Paste the URLs]

The AI will call the import and draft tools once per person. If a profile fails, ask it to show the reason and continue with the rest.

Draft from an existing conversation

The MCP can also read a conversation before preparing the next reply.

Find my conversation with [Name]. Read the recent messages and draft a reply.

The reply should:
- answer their last question directly
- use the context already in the thread
- stay under [length]
- avoid making promises I did not give you

Save the result as a TalkToHumans draft. Do not send it.

This uses linkedin_list_conversations, linkedin_get_conversation_messages, and linkedin_create_message_draft.

Review and send from TalkToHumans

Open the review link

The draft result includes an app_url. Open it to land on the right contact or conversation.

Check the person

Read the profile, tags, notes, company context, and any existing message history. Make sure the AI selected the right identity.

Rewrite the generic parts

Remove anything you would not have written yourself. Check names, claims, variables, links, and the reason for reaching out.

Choose the LinkedIn action

For an unconnected person, use the draft as a connection-request note when appropriate. For an existing conversation, send or schedule it as a message.

Add follow-up with care

If the contact deserves a structured follow-up, apply a sequence in the app and tailor each step to that person.

Why this is safer than direct AI sending

Importing a profile, creating a view, adding notes, and saving a draft change TalkToHumans workspace data. They do not send a LinkedIn action. The final action stays inside the browser-based TalkToHumans flow: → you see the person and the draft → you choose whether to send → the extension checks the active LinkedIn identity → LinkedIn actions pass through the normal pacing and daily limits You get the useful part of AI, which is research and preparation, without handing it an invisible send loop.

Importing a contact does not prove the person’s data is current. Review the LinkedIn profile and any freshness warning before relying on the result.

Team reports

Your manager asks how LinkedIn is going. “Busy” is probably not the report they had in mind. TalkToHumans gives admins the useful numbers without exposing private conversation content. Ask a question through the MCP or API, compare teammates or accounts, and let your AI turn the result into a table or graph. There is no in-app analytics dashboard yet. Your AI client is the reporting interface.

What you can measure

→ distinct people contacted → replies to outbound conversations → reply rate → connection requests sent and accepted → connection-request acceptance rate → activity by teammate and LinkedIn account → activity by channel, source, and event type → runs, replies, and rates for each template Filter by date range, account, user, event type, channel, or source.

What reporting does not contain

The activity ledger does not include message bodies, attachments, notes, drafts, or AI-generated copy. It tells a manager what happened without turning private DMs into surveillance data.

How rates are counted

Reply and acceptance rates use distinct contacted people and activated template runs. Re-syncing a message or receiving several replies in one conversation should not make the rate look better than it is.

Run a weekly manager report

Ask an authorized AI client:

Use TalkToHumans MCP to prepare our LinkedIn manager report for the last 7 days.

Show:
- people contacted
- replies and reply rate
- connection requests sent
- connection requests accepted and acceptance rate
- results by teammate
- results by LinkedIn account
- the 5 best and 5 weakest templates with enough runs to be useful

Call out missing attribution or unusually small samples.
Do not include or infer message content.

Build a trend graph

Ask the AI to call activity_summary once for each period you want to compare, then graph the results.

Build a 6-week LinkedIn activity report from TalkToHumans.

For each week, collect:
- people contacted
- reply rate
- connection requests sent
- connection-request acceptance rate

Create:
1. a line chart for weekly volume
2. a line chart for reply and acceptance rates
3. a short written explanation of the biggest changes

Keep zero values visible. Mark weeks with incomplete or unattributed data.

The client can create the chart in whatever format it supports, such as an inline graph, spreadsheet, notebook, or image.

Compare teammates without rewarding spam

Raw send volume is easy to game. Pair activity with outcomes and context.

Compare LinkedIn activity by teammate for the last 30 days.

Show volume, replies, reply rate, connection requests, and acceptance rate.
Do not rank people on volume alone.
Flag small samples and missing user attribution.
Suggest questions a manager should ask before drawing a conclusion.

Compare templates

Use TalkToHumans activity data to compare template performance over the last 30 days.

Only compare templates with enough activated runs to make the result useful.
Show runs started, replies, reply rate, connection requests, acceptances,
and acceptance rate. Separate message templates from connection-request flows.

The MCP tools

→ activity_list for filtered activity records.
→ activity_summary for grouped metrics and rates. Both tools accept date, account, user, event-type, channel, and source filters.

Report with the API

Use GET /v1/activity for filtered records and GET /v1/activity/summary for grouped metrics. Admins manage workspace API keys.

Get started

Manage contacts

Manage conversations

Common use cases

Account & Team

Advanced

Help

✨ Use with your AI tools

Find contacts

What the MCP can search

Find leads

Find hires

Find partners and warm paths

Save the shortlist in TalkToHumans

Keep the judgment human

Draft outreach

What the workflow can do

Import a list and prepare drafts

Draft from an existing conversation

Review and send from TalkToHumans

Why this is safer than direct AI sending

Team reports

What you can measure

What reporting does not contain

How rates are counted

Run a weekly manager report

Build a trend graph

Compare teammates without rewarding spam

Compare templates

The MCP tools

Report with the API

​Find contacts

​What the MCP can search

​Find leads

​Find hires

​Find partners and warm paths

​Save the shortlist in TalkToHumans

​Keep the judgment human

​Draft outreach

​What the workflow can do

​Import a list and prepare drafts

​Draft from an existing conversation

​Review and send from TalkToHumans

​Why this is safer than direct AI sending

​Team reports

​What you can measure

​What reporting does not contain

​How rates are counted

​Run a weekly manager report

​Build a trend graph

​Compare teammates without rewarding spam

​Compare templates

​The MCP tools

​Report with the API

Find contacts

What the MCP can search

Find leads

Find hires

Find partners and warm paths

Save the shortlist in TalkToHumans

Keep the judgment human

Draft outreach

What the workflow can do

Import a list and prepare drafts

Draft from an existing conversation

Review and send from TalkToHumans

Why this is safer than direct AI sending

Team reports

What you can measure

What reporting does not contain

How rates are counted

Run a weekly manager report

Build a trend graph

Compare teammates without rewarding spam

Compare templates

The MCP tools

Report with the API