linkedin-buying-signals
Find B2B prospects showing buying signals on LinkedIn and draft the outreach.
Should I use this
Quality & Safety
Findings (1)
- LOWTool 'get_prospect' description lacks action verbin get_prospect
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"linkedin-buying-signals": {
"url": "https://www.getcleed.com/api/mcp"
}
}
}Remote endpoints
https://www.getcleed.com/api/mcpstreamable-httpWhat it can do
Tool inventory
Tools (15)
🟢list_prospects(search, company, industry, title, country, ...)
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.
Input Schema
{
"type": "object",
"properties": {
"search": {
"type": "string",
"description": "Free text matched against first name, last name, company and title"
},
"company": {
"type": "string"
},
"industry": {
"type": "string"
},
"title": {
"type": "string",
"description": "Job title contains this text"
},
"country": {
"type": "string"
},
"signalType": {
"type": "string",
"description": "Only prospects carrying a signal of this type (see list_signals)"
},
"minSignals": {
"type": "integer",
"description": "Only prospects with at least this many signals"
},
"hasEmail": {
"type": "boolean"
},
"enrichmentStatus": {
"type": "string",
"enum": [
"pending",
"enriched",
"failed"
]
},
"notContacted": {
"type": "boolean",
"description": "Only prospects with no email sent yet"
},
"limit": {
"type": "integer",
"description": "How many to return (max 50)",
"default": 20
},
"offset": {
"type": "integer",
"default": 0
}
},
"additionalProperties": false
}🟢get_prospect(prospectId, linkedinUrl)
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.
Input Schema
{
"type": "object",
"properties": {
"prospectId": {
"type": "string",
"description": "The prospect id from list_prospects"
},
"linkedinUrl": {
"type": "string",
"description": "Their LinkedIn profile URL, if the id is unknown"
}
},
"additionalProperties": false
}🟢get_pipeline_stats
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.
Input Schema
{
"type": "object",
"properties": {},
"additionalProperties": false
}🟢list_signals(enabledOnly)
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.
Input Schema
{
"type": "object",
"properties": {
"enabledOnly": {
"type": "boolean",
"default": true
}
},
"additionalProperties": false
}🟢get_icp
The active ICP: target roles, industries, countries, company sizes, relevant keywords and any exclusions. This is what sourcing and scoring filter against.
Input Schema
{
"type": "object",
"properties": {},
"additionalProperties": false
}🟡add_leads_from_urls(linkedinUrls, listId)
Save LinkedIn profiles as prospects and enrich them (name, role, company, industry, location, language). Skips anyone already saved. Counts against the import quota.
Input Schema
{
"type": "object",
"properties": {
"linkedinUrls": {
"type": "array",
"items": {
"type": "string"
},
"description": "LinkedIn profile URLs"
},
"listId": {
"type": "string",
"description": "Optional list to add them to (see get_icp for list ids)"
}
},
"required": [
"linkedinUrls"
],
"additionalProperties": false
}🟢find_leads_from_signals(signalType, postsPerSignal)
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 candidates to import_sourced_leads. Spends a little of the monthly sourcing budget.
Input Schema
{
"type": "object",
"properties": {
"signalType": {
"type": "string",
"description": "Which signal to source for; omit to use the first enabled one (see list_signals)"
},
"postsPerSignal": {
"type": "integer",
"default": 3
}
},
"additionalProperties": false
}🟡import_sourced_leads(signalType, tokens, listId)
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.
Input Schema
{
"type": "object",
"properties": {
"signalType": {
"type": "string",
"description": "The signalType returned by find_leads_from_signals"
},
"tokens": {
"type": "array",
"items": {
"type": "string"
},
"description": "The token values of the candidates to import"
},
"listId": {
"type": "string"
}
},
"required": [
"signalType",
"tokens"
],
"additionalProperties": false
}🟢analyze_prospects(prospectIds, limit)
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.
Input Schema
{
"type": "object",
"properties": {
"prospectIds": {
"type": "array",
"items": {
"type": "string"
},
"description": "Which prospects to analyze; omit to take those with no signals yet"
},
"limit": {
"type": "integer",
"description": "Cap when prospectIds is omitted",
"default": 10
}
},
"additionalProperties": false
}🟡draft_email(prospectId, linkedinUrl, signalIndex, regenerate, language)
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.
Input Schema
{
"type": "object",
"properties": {
"prospectId": {
"type": "string"
},
"linkedinUrl": {
"type": "string"
},
"signalIndex": {
"type": "integer",
"description": "Which signal to lead with",
"default": 0
},
"regenerate": {
"type": "boolean",
"description": "Ignore any cached draft",
"default": false
},
"language": {
"type": "string",
"enum": [
"english",
"french"
]
}
},
"additionalProperties": false
}🟡draft_linkedin_message(prospectId, linkedinUrl, regenerate, language)
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.
Input Schema
{
"type": "object",
"properties": {
"prospectId": {
"type": "string"
},
"linkedinUrl": {
"type": "string"
},
"regenerate": {
"type": "boolean",
"default": false
},
"language": {
"type": "string",
"enum": [
"english",
"french"
]
}
},
"additionalProperties": false
}🔴send_email(prospectId, linkedinUrl, subject, body)
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.
Input Schema
{
"type": "object",
"properties": {
"prospectId": {
"type": "string"
},
"linkedinUrl": {
"type": "string"
},
"subject": {
"type": "string",
"description": "Omit to use the saved draft"
},
"body": {
"type": "string",
"description": "Omit to use the saved draft"
}
},
"additionalProperties": false
}🔴send_linkedin_message(prospectId, linkedinUrl, message)
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.
Input Schema
{
"type": "object",
"properties": {
"prospectId": {
"type": "string"
},
"linkedinUrl": {
"type": "string"
},
"message": {
"type": "string",
"description": "The note, max 300 characters. Omit to use the saved draft"
}
},
"additionalProperties": false
}⚪mark_email_sent(prospectId, linkedinUrl)
Record that a prospect was emailed outside getcleed, so reporting and follow-ups stay correct.
Input Schema
{
"type": "object",
"properties": {
"prospectId": {
"type": "string"
},
"linkedinUrl": {
"type": "string"
}
},
"additionalProperties": false
}🟡create_custom_signal(name, detects, exampleTitle, exampleDetail)
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.
Input Schema
{
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Short display name"
},
"detects": {
"type": "string",
"description": "What to look for, in plain language"
},
"exampleTitle": {
"type": "string"
},
"exampleDetail": {
"type": "string"
}
},
"required": [
"name",
"detects"
],
"additionalProperties": false
}Community
Evidence