Searches the public web for companies showing technical ICP signals for one of 72BPM's four practice areas, and returns a de-duplicated, signal-ranked candidate list.

This tool does NOT scrape LinkedIn or any social platform directly. It queries a real search engine (Brave Search) the same way a human researcher would, and reads back only the public snippet text already indexed. Use leadgen_enrich_company next to go deeper on any promising candidate.

Args:

• category ('saas' | 'e_mobility' | 'iot' | 'ai_agents'): which practice area to prospect for

• region (string, optional): geographic focus, e.g. "India", "UAE"

• extra_keywords (string[], optional): up to 5 extra terms to narrow the search

• max_results (number): 1-20, default 8

Returns: JSON with schema: { "category": string, "category_label": string, "queries_used": string[], "candidates": [ { "name": string, // best-guess company/page name from search result title "domain": string | null, // extracted domain, use with leadgen_enrich_company "matched_signals": string[],// ICP phrases found in the search snippet itself "sources": [{ "title": string, "url": string, "snippet": string }] } ], "count": number }

Examples:

• Use when: "Find e-mobility companies in India working on payment sync" -> category="e_mobility", region="India", extra_keywords=["payment sync"]

• Use when: "Who's building multi-agent systems in the UAE" -> category="ai_agents", region="UAE"

• Don't use when: you already have a company name and just want its tech profile (use leadgen_enrich_company instead)

Error Handling:

• Returns an error message if BRAVE_API_KEY is not configured, with a link to get one

• Returns "No candidates found" with the queries tried if the search turned up nothing usable

Builds a technical profile for a specific company by crawling its own public website (homepage, /careers, /about, /blog, /engineering) and cross-referencing an indexed LinkedIn snippet plus public job postings — all via legitimate, ToS-compliant sources (no LinkedIn scraping).

Returns which ICP signal phrases were found, for ALL FOUR practice areas at once, so you can see if a company is a multi-category fit (e.g. an IoT hardware company also hiring for LLM-based ops automation).

Args:

• company_name (string): required

• domain (string, optional): website domain; enrichment is much richer with it, but LinkedIn/job-posting signal still works without it

Returns: JSON with schema: { "company_name": string, "domain": string | null, "crawled_pages": string[], // URLs successfully crawled "crawl_errors": string[], // pages skipped/failed, with reason "linkedin_snippet": { "title": string, "url": string, "snippet": string } | null, "job_posting_hits": [{ "title": string, "url": string, "snippet": string }], "signals_by_category": { "": { "matched": [{ "phrase": string, "weight": number, "found_in": string[] }], "missing": string[] } } }

Examples:

• Use when: "What's ElectreeFi's tech stack look like?" -> company_name="ElectreeFi", domain="electreefi.com"

• Use when: you have a candidate from leadgen_discover_companies and want the full picture before scoring

• Don't use when: you just want a single category's numeric fit score (use leadgen_score_lead — it's cheaper and more direct)

Error Handling:

• If domain is unreachable, crawl_errors will explain why but the tool still returns LinkedIn/job-posting signal

• Returns an error if BRAVE_API_KEY is missing and no domain was provided (nothing to enrich from)

Computes a weighted 0-100 ICP fit score for one company against one 72BPM practice area, with a tier (hot/warm/cold) and a confidence level based on how many independent public sources corroborated the signal.

Internally this reuses the same public-source gathering as leadgen_enrich_company (and shares its short-lived cache, so calling enrich then score on the same company won't double the network calls).

Args:

• company_name (string): required

• domain (string, optional): website domain — scoring is more reliable with it

• category ('saas' | 'e_mobility' | 'iot' | 'ai_agents'): required, which practice area to score against

Returns: JSON with schema: { "company_name": string, "category": string, "category_label": string, "score": number, // 0-100 "tier": "hot" | "warm" | "cold", "confidence": "low" | "medium" | "high", // based on # of independent sources with matches "matched_signals": [{ "phrase": string, "weight": number, "found_in": string[] }], "missing_high_value_signals": string[], // top signals NOT found — good discovery-call questions "rationale": string }

Tier thresholds: score >= 55 = hot, >= 30 = warm, else cold. Confidence: 3+ independent sources = high, 2 = medium, 0-1 = low. Treat "low confidence + hot tier" as promising but unverified — worth a human look before outreach.

Examples:

• Use when: "Is Acme Corp a good IoT lead?" -> company_name="Acme Corp", domain="acme.com", category="iot"

• Don't use when: you want the full multi-category breakdown (use leadgen_enrich_company instead)

Error Handling:

• Returns an error if BRAVE_API_KEY is missing and domain gathers nothing

• Returns score=0, tier="cold", confidence="low" (not an error) if the company has no public signal at all — that's a valid, informative result

Searches public, search-engine-indexed snippets (not a LinkedIn scrape) for a likely engineering decision-maker at a company — CTO, VP Engineering, Head of Engineering, etc.

IMPORTANT: results are UNVERIFIED candidates parsed from search snippet text. They are a starting point for manual confirmation (open the profile URL yourself, or verify via a mutual connection / company site team page), not a guaranteed accurate contact. Never send outreach based solely on this tool's output without a human sanity-check.

Args:

• company_name (string): required

• role_hint (string, optional): a specific title to prioritize, e.g. "Head of Engineering". Defaults to trying CTO, VP Engineering, Head of Engineering, Engineering Lead, Director of Engineering.

Returns: JSON with schema: { "company_name": string, "candidates": [ { "name_guess": string | null, "title_guess": string | null, "profile_url": string | null, "snippet": string, "verified": false // always false — see IMPORTANT note above } ], "count": number }

Examples:

• Use when: "Who leads engineering at ElectreeFi?" -> company_name="ElectreeFi"

• Use when: "Find their Head of Data" -> company_name="Acme Corp", role_hint="Head of Data"

• Don't use when: you need a verified email address (this tool does not find or guess emails)

Error Handling:

• Returns "No candidates found" if search turns up nothing matching common engineering-leadership titles

• Returns an error if BRAVE_API_KEY is not configured

Drafts two personalized outreach email variants for a qualified lead, following 72BPM's value-lead framework: lead with a specific technical friction point (not a sales pitch), show parallel domain authority for the relevant practice area, and close with a low-friction peer-to-peer call to action.

This tool does NOT send anything — it only drafts text for you to review and send yourself.

Args:

• company_name (string): required

• category ('saas' | 'e_mobility' | 'iot' | 'ai_agents'): required, determines which authority/experience line is used

• friction_point (string): required — the specific technical gap to lead with. Pull this from leadgen_enrich_company's matched_signals or crawled content; generic friction points make for generic (worse) pitches

• contact_name (string, optional): personalizes the greeting

• contact_title (string, optional): for your own reference / future use

• sender_name (string, default "[Your name]"): signs the email

Returns: JSON with schema: { "company_name": string, "category": string, "variants": [ { "label": string, "subject": string, "body": string } ] }

Examples:

• Use when: you've scored a lead as "hot" and identified its top missing/matched signal, and want ready-to-edit email drafts

• Don't use when: you haven't identified a real, specific friction point yet — generic pitches convert poorly; go back to leadgen_enrich_company first

Error Handling:

• Returns a validation error if friction_point is missing or too short — this tool refuses to draft a pitch with nothing specific to say

Persists a qualified lead to the local pipeline store (a JSON file under ./data by default; see README for swapping in a real database for hosted/multi-user deployments).

Args:

• company_name (string): required

• domain, region (string, optional)

• category ('saas' | 'e_mobility' | 'iot' | 'ai_agents'): required

• score (number 0-100, optional): from leadgen_score_lead

• tier ('hot' | 'warm' | 'cold', optional): from leadgen_score_lead

• notes (string, optional)

• contact_name, contact_title, contact_profile_url (string, optional): from leadgen_find_contact

Returns: the saved lead record including its generated "id", which you'll need for leadgen_update_lead.

Examples:

• Use when: you've scored a company as hot/warm and want to track it for follow-up

• Don't use when: you're just browsing candidates (save only once you're tracking it for real)

Lists saved leads from the pipeline, optionally filtered by category, tier, or status.

Args:

• category, tier, status (all optional): filter the results

Returns: JSON: { "leads": StoredLead[], "count": number } where StoredLead has fields: id, companyName, domain, category, region, score, tier, status, notes, contact, createdAt, updatedAt

Examples:

• Use when: "Show me all hot e-mobility leads" -> category="e_mobility", tier="hot"

• Use when: "What's still uncontacted?" -> status="new"

Updates a saved lead's status and/or notes — e.g. after sending outreach or getting a reply.

Args:

• lead_id (string, uuid): required, from leadgen_save_lead or leadgen_list_leads

• status ('new'|'researching'|'contacted'|'replied'|'qualified'|'disqualified', optional)

• notes (string, optional)

At least one of status or notes must be provided.

Returns: the updated lead record, or an error if lead_id doesn't exist.