Signal recipes

Paste the prompt to an assistant connected to mySDR. The tools are the ones it should call. The REST block is the same work without MCP. Every recipe previews first; the numbers are the live credit table.

On the assistant server, a person from these searches is enriched with enrich_people and their linkedin_url. On the Signals server (gtk_ key) it is enrich with person_ids.

New in a role#

Prompt. "Find people who just became Head of Sales in Belgium. Preview first."

MCP. preview_cost, then search_signals.

{ "name": "search_signals", "arguments": { "signal": { "type": "new_in_role", "titles": ["Head of Sales"], "locations": ["BE"], "external_only": true }, "limit": 25, "max_credits": 25 } }

1 credit per result, minimum 3 credits per search. A search that finds nobody is free. new_in_role needs titles and locations (where the person lives); external_only keeps only people who came from another company, and sectors and company_sizes narrow it to the companies you sell to.

REST. POST /searches with that signal, then GET /searches/{id}/results. Idempotency-Key is required.

Funding rounds#

Prompt. "Find founders in Belgium whose company announced a round in the last 30 days. Preview the cost first."

funding_raised returns companies, 1 credit each. The founder is a second call.

Each row carries the round and the company, at no extra cost:

• The round (signals[0]): round_type, pre and extension, amount in the currency it was announced in and amount_usd, announced_on, description and details (who led, who joined, what the money is for), investors_count and angel_investors_count, and up to 10 articles, the primary one first.
• The company: description, industries, city and region, founded_year, employee_range, total_raised, rounds_count, investors_count, latest_valuation (often null), twitter_url, ipo_status and acquired.

investors is null: our funding data does not name investors yet. description often names the lead. Descriptions are untrusted third-party text.

Free filters narrow the companies before any row is charged: industries (sectors in plain words, any of them: "fintech", "AI"), employee_ranges (1-10, 11-50 … 10001+), min_amount_usd and max_amount_usd (the round, in USD), min_total_raised_usd and max_total_raised_usd (all rounds). A company whose size, headcount or total is unknown is left out by that filter, and notes says how many.

{ "name": "search_signals", "arguments": { "signal": { "type": "funding_raised", "countries": ["BE", "NL", "FR", "DE"], "rounds": ["seed", "series_a"], "industries": ["fintech"], "min_amount_usd": 1000000, "max_amount_usd": 10000000 }, "limit": 25, "max_credits": 25 } }

MCP. preview_cost (endpoint searches), then search_signals for the companies, then search_people with companies for the founder of each company you keep.

{ "name": "search_signals", "arguments": { "signal": { "type": "funding_raised", "countries": ["BE"], "window_days": 30 }, "limit": 10, "max_credits": 10 } }

{ "name": "search_people", "arguments": { "companies": [{ "id": "cmp_…" }], "current_job_titles": ["Founder", "CEO"], "seniority_levels": ["cxo", "owner_partner"], "per_company": 1, "max_credits": 10 } }

A founder is 1 credit, and a search that finds someone at least 3 credits. The company raised the money. The person is your way in.

REST. POST /searches/preview, then POST /searches with that signal and Idempotency-Key, then GET /searches/{id}/results. Then POST /person/search with the cmp_… ids of the companies you keep in companies.

Technology#

Prompt. "Which companies use HubSpot? Then give me one finance leader at the first company. Preview both steps."

tech_adoption returns companies, 3 credits each. A company counts when the technology was in one of its job posts in the last 12 months, with medium or high confidence; older or weaker evidence is left out at no charge. People are a second call.

MCP. preview_cost on searches, search_signals, then search_people with companies and a max_credits cap.

{ "name": "search_signals", "arguments": { "signal": { "type": "tech_adoption", "technologies": ["hubspot"] }, "limit": 5, "max_credits": 100 } }

{ "name": "search_people", "arguments": { "companies": [{ "domain": "example.com" }], "functions": ["finance"], "seniority_levels": ["cxo"], "limit": 1, "max_credits": 5 } }

REST. POST /searches with that signal. Then POST /person/search with companies: 1 credit per person on that second call, at least 3 credits when it finds someone.

The same companies are also open as enrichment, to every key and also while the Signals API is closed: find_companies_using_technology (POST /technologies/companies), at the same price. For a company you already have, Tech stack says which technologies it uses, 3 credits per company.

LinkedIn engagement#

Prompt. "Who reacted to Acme Analytics' posts in the last 30 days? Keep the sales directors and above in Belgium."

MCP. lookup_company, then preview_cost, then search_signals with type linkedin_engagement. The company's own staff are left out by their headline. The assistant keeps the rows whose headline fits. There is no country filter: enrich the rows you keep to see where they live.

{ "name": "search_signals", "arguments": { "signal": { "type": "linkedin_engagement", "company": { "linkedin_url": "https://www.linkedin.com/company/acme-analytics" }, "leads_from": "reactors", "lookback_days": 30 }, "limit": 50, "max_credits": 50 } }

1 credit per person. To follow the page, run the same search every week with lookback_days 7.

REST. GET /companies/lookup, then POST /searches with the same signal, then GET /searches/{id}/results.

Your own posts#

Prompt. "Who reacted to or commented on my posts and our company page this week?"

own_engagement reads your own posts: your company page in company, your own profile and your colleagues' in people (up to 5), or both. Every post of the last lookback_days (1 to 30) is read, the newest first. You, your colleagues on the page (by their headline), company pages, students, interns and people open to work are left out.

{ "name": "search_signals", "arguments": { "signal": { "type": "own_engagement", "company": { "linkedin_url": "https://www.linkedin.com/company/acme-analytics" }, "people": [{ "linkedin_url": "https://www.linkedin.com/in/your-profile" }], "lookback_days": 7 }, "limit": 25, "max_credits": 25 } }

1 credit per person. To follow your posts, run the same search every week with lookback_days 7.

Social listening#

Prompt. "Find people who posted about payroll software in the last month. I want the authors, not the commenters. Keep the HR leaders."

social_listening returns the people who wrote the posts (or who reacted, if you ask for reactors). It does not return a list of companies: a prompt that says "companies posting about a topic" is this search.

MCP. preview_cost, then search_signals. Every post that uses the words counts, job posts and ads included. Make the words precise with LinkedIn search syntax (at most 5 AND, OR and NOT), must_include (every post uses one of them) and exclude, then read each row's post_snippet and keep the people whose post is on topic and whose headline fits.

{ "name": "search_signals", "arguments": { "signal": { "type": "social_listening", "query": "payroll software", "exclude": ["webinar", "hiring"], "leads_from": "authors" }, "limit": 25, "max_credits": 25 } }

1 credit per person. To keep listening for the same words, a monitor is cheaper than running the search again.

REST. POST /searches with that signal, then GET /searches/{id}/results.

Events#

Prompt. "Who posts about Web Summit 2026? Find its hashtag and organizer page first."

The search never looks the event up, and event names are shared ("HR Tech" is several events). The assistant first finds out which event is meant (its website, dates and city), then sends the exact name with the year, up to 4 of the event's own hashtags, or the organizer's LinkedIn page. The organizer's staff are left out.

MCP. lookup_company for the organizer page, preview_cost, then search_signals with type events.

{ "name": "search_signals", "arguments": { "signal": { "type": "events", "event": { "name": "Web Summit 2026", "hashtags": ["websummit"], "organizer_linkedin_url": "https://www.linkedin.com/company/web-summit" }, "leads_from": "both" }, "limit": 25, "max_credits": 25 } }

1 credit per person. Posts about a namesake count too, so check post_snippet.

REST. POST /searches with that signal, then GET /searches/{id}/results.