prospect

Prospect

Go from an ICP description to a ranked, phone-enriched lead list. Filters are resolved before search — never guess filter values.

Step 1 — Parse the ICP

Extract structured filters from the user's natural language description. Some filters take free-form text directly; others must be resolved to canonical values first.

Contact filters (prospecting_contact_search):

• Job titles → pass directly as jobTitles (free-form strings, e.g. "VP of Sales"). No resolution call needed.
• Department / seniority → resolve via prospecting_contact_filters (type: departments, seniority). Use these for broad role targeting when a specific title isn't given.
• Country → resolve via prospecting_contact_filters (type: all_countries); Location → type: locations (requires locationSearchText).

Company filters (prospecting_company_search):

• Industry → resolve via prospecting_company_filters (type: industries_labels)
• Size → resolve via prospecting_company_filters (type: sizes)
• Revenue → resolve via prospecting_company_filters (type: revenues)
• Location → resolve via prospecting_company_filters (type: locations, requires q)
• Tech stack → resolve via prospecting_company_filters (type: technologies, requires q)
• Buying intent → resolve via prospecting_company_filters (type: intent_topics)

Resolve every non-title filter to canonical values before searching — passing raw natural-language strings as structured filter values is the most common cause of search failures. Each prospecting_*_filters call resolves one filter type; run the independent lookups in parallel.

If the ICP is too vague to resolve (no title, no industry, no company size), ask one clarifying question before proceeding. At minimum, a title or department and at least one company-level constraint are required.

See references/filter-guide.md for filter resolution details.

Step 2 — Search Companies

Use prospecting_company_search with resolved company filters. Request up to 25 results. This scopes the contact search to qualified accounts.

If the user only specified contact-level criteria (no company filters), skip this step and go directly to Step 3.

Step 3 — Search Contacts

Use prospecting_contact_search with resolved contact filters. Scope to the company results from Step 2 where applicable. Request up to 25 results.

Step 4 — Enrich Top Results

Search results are previews — they carry no phones/emails but include a canReveal[] list per contact showing which fields can be revealed and their per-field credit cost in canReveal[].credits.

Use prospecting_contact_enrich with the contact ids to reveal phones and email. Pass reveal set from the results' canReveal[].field to control exactly which fields (and credits) you pay for. Up to 50 contacts per call — split larger sets across calls.

Before enriching, sum the canReveal[].credits for the fields you'll reveal, state the total to the user, and wait for confirmation on large batches. Use account_usage first if the user wants to confirm their balance covers it.

Step 5 — Present the Lead List

Filters Applied

Show the user exactly what was used so they can verify:

Filter	Value
...	...

Lead List

#	Name	Title	Company	Industry	Size	Direct Phone	Mobile	Email	Intent Signal

• Surface direct phone and mobile as separate columns — do not merge or hide them
• Mark missing phone numbers with — not blank cells
• Include intent signal column only if intent_topics filter was used

Summary

• Results found: X (showing top Y)
• Contacts with verified phone: Z
• Credits consumed: N

Step 6 — Offer Next Actions

1. Refine — adjust filters and re-run
2. Add intent filter — narrow to companies actively researching a topic
3. Add tech stack filter — narrow to companies using a specific technology
4. Run signal-prospect — cross this list against current buying signals
5. Export — format as CSV for copy-paste