MCP server

Linkedin Buying Signals

com.getcleed/linkedin-buying-signals

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Find B2B prospects showing buying signals on LinkedIn and draft the outreach.

Website Source

At a glance

Type

MCP server

Publisher

getcleed.com (verified by the MCP Registry)

Status

Working at last check

Tools

16

Token cost

About 2,500 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http linkedin-buying-signals https://www.getcleed.com/api/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.linkedin-buying-signals]
url = "https://www.getcleed.com/api/mcp"

Gemini CLI

{"mcpServers":{"linkedin-buying-signals":{"httpUrl":"https://www.getcleed.com/api/mcp"}}}

Cursor

{"mcpServers":{"linkedin-buying-signals":{"url":"https://www.getcleed.com/api/mcp"}}}

VS Code

{"servers":{"linkedin-buying-signals":{"type":"http","url":"https://www.getcleed.com/api/mcp"}}}

Antigravity

agy mcp add linkedin-buying-signals https://www.getcleed.com/api/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"linkedin-buying-signals","server_url":"https://www.getcleed.com/api/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"linkedin-buying-signals","server_url":"https://www.getcleed.com/api/mcp"}

Tools

• add_leads_from_urls ~147 tokens
  Save LinkedIn profiles as prospects and enrich them (name, role, company, industry, location, language). Skips anyone already saved. Counts against the import quota.
• analyze_prospects ~176 tokens
  Run the signal pipeline on prospects: enrich anyone missing company data, read their recent LinkedIn activity, and detect buying signals. Every signal kept must quote its source verbatim. Counts against the monthly analysis quota. Slow: expect up to a few minutes.
• create_custom_signal ~158 tokens
  Teach the account a new buying signal to look for, described in plain language (for example "people complaining their onboarding takes too long"). Future analyses and sourcing will use it.
• draft_email ~163 tokens
  Write a personalised email for a prospect, built from their detected signals, in their language. Saves the draft on the prospect and returns it. Does not send.
• draft_linkedin_message ~170 tokens
  Write a short LinkedIn connection note for a prospect from their detected signals, in their language. Saves the draft and returns it. Does not send. Drafts follow the house rules (one fact about them, one question, no pitch, under 200 characters) and so must any note passed to send_linkedin_message.
• find_leads_from_signals ~196 tokens
  Search recent LinkedIn posts on the topics of this account's own signal definitions, keep only posts a check can prove are on topic, and return the people who wrote, commented on or reacted to them, filtered against the ICP (role, industry, country) and ranked. Nothing is saved: pass the returned ca
• get_icp ~77 tokens
  The active ICP: target roles, industries, countries, company sizes, relevant keywords and any exclusions. This is what sourcing and scoring filter against.
• get_pipeline_stats ~104 tokens
  How the pipeline stands: how many prospects, how many carry signals, enrichment state, how many have an email address, how many were contacted, what was added in the last 7 days, the most common signal types, and the remaining plan quota.
• get_prospect ~150 tokens
  Everything known about one prospect: role, company, location, language, every detected signal with the verbatim quote from its source and the LinkedIn URL it came from, any existing email or LinkedIn draft, and outreach state.
• get_sample_pipeline ~125 tokens
  A fictional pipeline in exactly the shapes the real tools return: an ICP, the signal definitions, pipeline stats and six prospects with their signals, verbatim quotes and drafts. Works without any credential, so an agent can show what Getcleed produces before an account exists. Nothing in it is a re
• import_sourced_leads ~173 tokens
  Save candidates returned by find_leads_from_signals. They arrive already enriched and with the signal attached, including the verbatim quote from the post that surfaced them. Counts against the import quota.
• list_prospects ~338 tokens
  Search the saved prospects in the account. Filter by company, industry, job title, country, signal type, minimum number of signals, whether an email address is known, enrichment state, or whether they have been contacted. Ordered by signal count, then most recently saved.
• list_signals ~114 tokens
  The buying signals this account looks for: the predefined ones and the custom ones, each with the definition used to detect it and whether it is enabled. Use the returned type values with list_prospects and find_leads_from_signals.
• mark_email_sent ~80 tokens
  Record that a prospect was emailed outside getcleed, so reporting and follow-ups stay correct.
• send_email Sends messages · ~165 tokens
  Send an email to a prospect from the connected mailbox. This reaches a real person immediately and cannot be undone. Pass the subject and body you want sent, or omit them to send the saved draft. Requires a mailbox connected in getcleed under Integrations.
• send_linkedin_message Sends messages · ~164 tokens
  Send a LinkedIn connection request with a note to a prospect from the connected LinkedIn account. This reaches a real person immediately and cannot be undone. Pass the note you want sent, or omit it to use the saved draft. Requires LinkedIn connected in getcleed under Integrations.

Browse

• Email
• Social media
• Company data

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