io.github.enzoemir1/leadpipe-mcp

View or update the global lead scoring configuration used by lead_score. Call with no fields (empty object) to fetch the current config. Pass any subset of fields to patch-update: six dimension weights (each 0–1, should sum to ~1 but not enforced), high_value_titles (string array), high_value_industries (string array), preferred_company_sizes, and custom_rules (array of {name, condition, points}). Changes apply to future lead_score calls only — previously scored leads keep their scores until re-scored.

Add 1 to 100 leads in a single call. Each lead uses the same schema as lead_ingest. Returns {ingested: Lead[], skipped: Array<{email, reason}>} — duplicates are skipped (not failed) so a partial batch still succeeds. Prefer this over repeated lead_ingest calls for bulk imports (CSV/webhook drops).

Populate the pipeline with a realistic demo dataset: 14 leads across 5 archetypes (hot decision-makers, warm mid-level, cold junior/small-co, raw unenriched, and disqualified). Each lead has appropriate enrichment state, scoring breakdown, and status, so every downstream tool — lead_list, lead_search, lead_score, crm_export, and the pipeline-overview resource — returns meaningful output immediately. Use this to evaluate LeadPipe via MCP Inspector without Hunter, HubSpot, or Pipedrive API keys. Safe to call multiple times; each call appends a fresh batch with new UUIDs. Returns counts by status plus sample_lead_ids you can feed into lead_enrich, lead_score, or crm_export.

Derive and attach company data to an existing lead using the email domain: company name, industry, size, country, website, estimated headcount, and common tech stack. Does not call external APIs — enrichment is driven by the built-in domain knowledge base. Updates the lead in place and returns the enriched record, ready for lead_score. Run this before lead_score for the best qualification accuracy.

Push leads to an external destination. target must be one of "hubspot", "pipedrive", "google_sheets", "csv", or "json". For CRM targets (hubspot, pipedrive) the respective API key env var must be set (HUBSPOT_API_KEY, PIPEDRIVE_API_TOKEN) — if missing, the tool returns a dry-run payload instead of erroring. Filter the export via lead_ids (explicit list) or min_score (everything above threshold). Returns {target, count, summary, errors?}.

Add a single lead to the pipeline. Required: email. Optional: first_name, last_name, job_title, company_name, phone, source ("website"|"linkedin"|"referral"|"event"|"cold_outreach"|"partner"|"other"), tags (string array), custom_fields. Returns the stored lead object with a generated UUID, initial status="new", created_at, and a null score (run lead_score to populate). Throws a duplicate error if the email is already in the pipeline — use lead_search first if you need upsert behaviour.

Filter leads against your Ideal Customer Profile BEFORE spending enrichment credits. Uses only locally-available signals (email domain, job_title, country, industry hints, tech_stack) so nothing is charged to Hunter.io, HubSpot, Pipedrive, or any other external service. Set auto_disqualify=true to also update rejected leads to status="disqualified" with the reject reasons stored in custom_fields. If lead_ids is omitted, evaluates every lead currently in status="new". Pairs naturally with upstream platform-detection tools (e.g. Detecto's detect_platform) — run that first to populate company.tech_stack, then run lead_qualify with required_tech_stack=["shopify"] to drop wrong-platform leads before they cost a single API call. Returns qualified/rejected counts, per-lead reasons, and an estimated credit savings figure.

Compute a 6-dimensional qualification score (0-100) for a lead: job_title, company_size, industry, engagement, recency, and custom_rules. Each dimension is weighted via config_scoring; the final score is their weighted average. Updates the lead status to "qualified" (≥60) or "disqualified" (<60) and stores score_breakdown alongside the total. Returns the updated lead with the breakdown. Run lead_enrich first for the most accurate industry/size signals.

Search and filter the lead pipeline. Optional filters: query (free-text over name/email/company), status ("new"|"qualified"|"disqualified"|"contacted"|"converted"), min_score, max_score, source, tags (array), date_from/date_to. Pagination via limit (default 50, max 200) and offset. Returns {total, leads[]}. Use this to drive exports, targeted scoring, and dashboards.

Portfolio-wide pipeline analytics across all leads. Returns {total_leads, leads_today, leads_this_week, leads_this_month, avg_score, qualified_rate (percent), by_status (counts per status), by_source (counts per source), score_distribution}. Takes no input — always aggregates the full dataset. Ideal for dashboards, stand-ups, and conversion-rate tracking.