# MCP Data Enrichment > Orchestrates data preparation and enrichment for contacts in Leadspicker projects through the Leadspicker MCP server. Runs built-in enrichment columns (email finding, email validation, LinkedIn data, company website summaries, department headcounts) and AI-powered magic columns in the right sequence via MCP tools. Use this skill whenever the user wants to enrich, prepare, or augment contact data in Leadspicker — for example "obohat kontakty o emaily", "enrichni projekt o LinkedIn data", "prepare data for outreach", "add company info to my leads", "find emails for my contacts", "run full enrichment pipeline", or any request to add data columns to a Leadspicker project. Triggers on Leadspicker enrichment, data preparation, contact augmentation, or running multiple enrichment steps in sequence — even when the user just says "prepare my data" or "enrich my leads" in the context of Leadspicker. - Skill: `tomasblatak/mcp-data-enrichment` (Agent Skill) - Install (CLI): `npx skillmds@latest add tomasblatak/mcp-data-enrichment` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomasblatak/mcp-data-enrichment/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tomasblatak (https://skillmd.com/u/tomasblatak) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/tomasblatak/mcp-data-enrichment --- # Leadspicker Data Enrichment (MCP) Prepares and enriches contact data in Leadspicker projects through the Leadspicker MCP server. Each enrichment creates a magic column that Leadspicker populates asynchronously for every contact in the project. This skill works in two modes: 1. **Selective mode** — user names specific columns (e.g., "enrich LinkedIn company description and website summary"). Run only those, plus any prerequisites they depend on. 2. **Pipeline mode** — user describes a goal (e.g., "prepare data for outreach"). Pick a recommended pipeline. --- ## MCP tools used | MCP tool | Purpose | | --------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `mcp__leadspicker__list_projects` | Look up project by name (`search_query`) or list recent (`order_by="last_active"`, `limit=20`) | | `mcp__leadspicker__get_project` | Fetch project detail by `project_id` | | `mcp__leadspicker__enrich_contacts` | Create an enrichment / AI magic column. Parameters: `project_id`, `magic_column_type`, `column_name`, optional `prompt`, optional `is_boolean`, optional `model` | | `mcp__leadspicker__list_magic_columns` | List existing magic columns in a project | | `mcp__leadspicker__update_magic_column` | Rename an existing magic column | | `mcp__leadspicker__list_contacts` | Fetch a page of contacts (used to size the project: `page=1, page_size=1` returns `count`) | If you need an endpoint not covered above, use `mcp__leadspicker__leadspicker_api_call` and introspect via `mcp__leadspicker__leadspicker_api_spec`. For everything in this skill the curated tools are enough. --- ## User input Always confirm these before running anything: | Parameter | Description | Example | | ------------------ | --------------------------------------------- | -------------------------------------------------------------------------------- | | `project_id` | Target Leadspicker project (resolved by name) | `26256` | | Enrichment request | Specific columns OR a goal | `"LinkedIn Company Description and Website Summary"` or `"prepare for outreach"` | ### Project resolution **Display format:** always render projects as **`{name} (#{id})`** — name first, ID in parentheses. 1. **User named a project** → call `mcp__leadspicker__list_projects(search_query="{name}", limit=5)`. If multiple match, ask which one. 2. **User did not name a project** → call `mcp__leadspicker__list_projects(order_by="last_active", limit=20)` and ask the user to pick. Use the resolved project's `id` for subsequent calls. --- ## ⚠️ Credit discipline Every enrichment column costs credits. Default to a **lean minimum**, never auto-enrich "just in case". Add optional columns only when the user's persona / criteria explicitly require them. **Lean default for outreach prep:** | Always | Only if user explicitly mentions | | --------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | | `company_name`, `company_website`, `company_linkedin`, `person_linkedin`, `li_full_name`, `person_position` (Core Identity) | `person_country` — person geography | | `li_company_description` | `li_company_country` — company geography | | `website_text_summary` | `li_company_size` (**range, never the exact count**) — size band | | `enrich_emails` + `validate_emails` (for email outreach) | `li_company_people_*` — specific department headcount | | | `company_founded_year` — company age / stage | | | `li_company_employee_count` — only if user **explicitly** asks for exact count (very rare; prefer the range) | | | `li_about_me`, `present_experience`, `past_experience`, `li_city`, `education_summary`, `linkedin_skills` — person-level personalization needs them | | | `li_latest_posts` — icebreaker from LinkedIn activity (**expensive — confirm with user first**) | **Hard rules:** - **NEVER default to `li_company_employee_count`.** Always prefer `li_company_size` (range like `51-200`). - **NEVER default to `li_latest_posts`.** It is the most expensive enrichment. - Before launching, parse the user's request for triggers (geography, size, department, age) and include the matching optional column only if triggered. --- ## Dependency map Some columns need other columns to exist first. When the user requests a column, auto-include its prerequisites. ``` No prerequisites: company_name, company_website, company_linkedin, person_linkedin, li_full_name, person_position, person_country, li_location, li_city, li_company_size, li_company_country, company_founded_year, li_company_employee_count, enrich_emails, linkedin_premium, is_first_degree_connection, followers_count, linkedin_skills Needs person_linkedin: li_about_me, li_latest_posts, present_experience, past_experience, education_summary Needs company_linkedin: li_company_description, all li_company_people_* department headcounts Needs company_website: website_text_summary Needs enrich_emails: validate_emails, email_mx Needs data from earlier enrichments (for AI context): any ai_custom_column (classification, icebreakers, cleaning, …) ``` **Selective resolution example.** User: "I want LinkedIn Company Description and Company Website Summary": 1. `li_company_description` needs `company_linkedin` 2. `website_text_summary` needs `company_website` 3. Neither prerequisite has its own prerequisite Result — run in this order: - Batch 1 (prerequisites): `company_linkedin`, `company_website` - Batch 2 (requested): `li_company_description`, `website_text_summary` Always show this resolved plan to the user before executing. --- ## Available enrichment columns Each enrichment is created with `mcp__leadspicker__enrich_contacts(project_id, magic_column_type, column_name)`. AI columns also pass `prompt` and `is_boolean`. Built-in columns don't need a prompt. ### Person data (from LinkedIn) | Column name | `magic_column_type` | Variable | What it does | | -------------------------- | ---------------------------- | -------------------------------- | ----------------------------- | | Full Name | `li_full_name` | `{{full_name}}` | Clean full name from LinkedIn | | Position | `person_position` | `{{position}}` | Current job title | | Person Country | `person_country` | `{{person_country}}` | Country from LinkedIn profile | | LinkedIn Location | `li_location` | `{{linkedin_location}}` | Location string from LinkedIn | | LinkedIn City | `li_city` | `{{linkedin_city}}` | City extracted from LinkedIn | | LinkedIn About Me | `li_about_me` | `{{linkedin_about_me}}` | LinkedIn bio / summary | | LinkedIn Latest Posts | `li_latest_posts` | `{{linkedin_latest_posts}}` | Recent LinkedIn post content | | Present Positions Summary | `present_experience` | `{{present_experiences}}` | Current roles summary | | Past Positions Summary | `past_experience` | `{{past_experiences}}` | Previous roles summary | | Education Summary | `education_summary` | `{{education_summary}}` | Education background | | LinkedIn Skills | `linkedin_skills` | `{{linkedin_skills}}` | Listed skills | | LinkedIn Followers | `followers_count` | `{{followers_count}}` | Follower count | | Is LinkedIn Premium | `linkedin_premium` | `{{linkedin_premium}}` | Premium account flag | | Is First Degree Connection | `is_first_degree_connection` | `{{is_first_degree_connection}}` | Connection degree check | | Person LinkedIn Profile | `person_linkedin` | `{{linkedin}}` | LinkedIn profile URL | ### Company data | Column name | `magic_column_type` | Variable | What it does | | ---------------------------- | --------------------------- | ---------------------------------- | --------------------------------- | | Company Name | `company_name` | `{{company_name}}` | Resolved company name | | Company Website | `company_website` | `{{company_website}}` | Company website URL | | Company LinkedIn Profile | `company_linkedin` | `{{company_linkedin}}` | Company LinkedIn page URL | | LinkedIn Company Description | `li_company_description` | `{{linkedin_company_description}}` | Company description from LinkedIn | | LinkedIn Company Size | `li_company_size` | `{{linkedin_company_size}}` | Size range (e.g. 51-200) | | LinkedIn Company Country | `li_company_country` | `{{linkedin_company_country}}` | HQ country | | Company Founded Year | `company_founded_year` | `{{company_founded_year}}` | Year founded | | Company Employee Count | `li_company_employee_count` | `{{company_employee_count}}` | Exact employee count | | Company Website Summary | `website_text_summary` | `{{website_text_summary}}` | AI summary of company website | ### Department headcounts | Column name | `magic_column_type` | Variable | | ------------------------------ | ---------------------------------------- | --------------------------------------------------- | | Sales Headcount | `li_company_people_sales` | `{{linkedin_people_category_sales}}` | | Engineering Headcount | `li_company_people_engineering` | `{{linkedin_people_category_engineering}}` | | Marketing Headcount | `li_company_people_marketing` | `{{linkedin_people_category_marketing}}` | | Research Headcount | `li_company_people_research` | `{{linkedin_people_category_research}}` | | Operations Headcount | `li_company_people_operations` | `{{linkedin_people_category_operations}}` | | Human Resources Headcount | `li_company_people_human_resources` | `{{linkedin_people_category_human_resources}}` | | Business Development Headcount | `li_company_people_business_development` | `{{linkedin_people_category_business_development}}` | ### Email | Column name | `magic_column_type` | Variable | What it does | | -------------------------- | ------------------- | -------------------------------------------- | ---------------------------------------- | | Enrich Emails | `enrich_emails` | `{{email}}` | Finds email addresses for contacts | | Validate Emails | `validate_emails` | `{{email}}` | Verifies deliverability | | Email MX (host + provider) | `email_mx` | `{{email_mx_host}}`, `{{email_mx_provider}}` | Resolves MX host and classifies provider | ### AI-powered columns (custom) `magic_column_type: "ai_custom_column"` with a `prompt`. They reference any variable from the columns above. For prompt-writing details, use the **mcp-classification** skill. Common AI enrichments: `Clean First Name`, `Clean Company Name`, `Job Title Cleaning`, `Company Industry`, `B2B/B2C`, `Icebreaker — Latest LI Posts`, `LinkedIn Post Summarization`, `Targeted Job Titles`, `First Name Vocative — CZ`, `Last Name Vocative — CZ`. ### Other built-in types | Column name | `magic_column_type` | What it does | | ------------ | ------------------- | ------------------------------------------------------------------------------ | | HTTP Request | `http_request` | Calls an HTTP endpoint per contact and extracts a value from the JSON response | --- ## Recommended pipelines Order matters — later steps often depend on earlier ones. Show the picked pipeline to the user, confirm, then execute. ### Lean Outreach Prep — DEFAULT for "prepare for outreach" Minimum column set for classification + personalization + email outreach. Start here; add optional columns from the credit-discipline table only when triggered. ``` Batch 1 — Core Identity (ALWAYS): company_name, company_website, company_linkedin, person_linkedin, li_full_name, person_position Batch 2 — Classification context (ALWAYS): li_company_description, website_text_summary Batch 2b — Conditional company-level (only if triggered): li_company_country — user cares where the company is based li_company_size (RANGE) — user cares about employee category / size band li_company_people_* — user mentioned that department company_founded_year — user mentioned company age / stage li_company_employee_count — only if user explicitly asked for exact count Batch 3 — Conditional person-level (only if triggered): person_country — user cares where the person is based li_about_me — personalization depends on bio present_experience — personalization uses role tenure li_latest_posts — icebreaker uses post activity (EXPENSIVE — confirm first) Batch 4 — Email (ALWAYS for email outreach): enrich_emails, then validate_emails Batch 5 — AI columns: any classification / scoring / personalization (ai_custom_column). Hand off to mcp-classification or mcp-personalizer skills. ``` Show the plan with three sections: **Always**, **Added because you mentioned X**, **NOT enriching (saves credits)**. Only proceed after explicit confirmation. ### Quick email enrichment ``` Batch 1: enrich_emails Batch 2: validate_emails (after batch 1 completes) ``` ### Company research ``` Batch 1: company_name, company_website, company_linkedin Batch 2: li_company_description, website_text_summary, li_company_size, company_founded_year (li_company_employee_count only if asked) Batch 3: li_company_people_* as needed ``` ### LinkedIn person enrichment ``` Batch 1: person_linkedin, li_full_name, person_position Batch 2: li_about_me, li_latest_posts, present_experience, past_experience, education_summary, linkedin_skills ``` ### Outreach prep (CZ market) ``` Batch 1–4: Lean Outreach Prep (with credit discipline) Batch 5: AI custom columns: Clean First Name, First Name Vocative — CZ, Clean Company Name, any classification columns, Icebreaker — Latest LI Posts (only if li_latest_posts was enriched) ``` For prompt details on AI columns, use **mcp-personalizer** (vocatives, hooks, salutations) or **mcp-classification** (boolean / string / scored classifications). ### Full historical enrichment (legacy) Use only when the user explicitly says "run full enrichment" and reconfirms after the cost warning. Otherwise prefer Lean Outreach Prep. ``` All of Lean Outreach Prep, plus: li_company_size, li_company_country, company_founded_year, li_company_employee_count, li_about_me, li_latest_posts, present_experience, li_city, person_country ``` --- ## Workflow ### Step 1 — collect input Resolve the project (see **Project resolution**) and gather the enrichment request. ### Step 2 — determine mode and build the plan **Selective mode** (user named specific columns): 1. Match the user's words to columns in the tables above 2. Look up the `magic_column_type` (this is what `mcp__leadspicker__enrich_contacts` needs) 3. Check the dependency map — add missing prerequisites 4. Group into batches: prerequisites first, then requested 5. Present the plan separating prerequisites from requested columns Example to show user: ``` I'll enrich these columns for Marketing Q4 (#26256): Prerequisites (needed first): • Company LinkedIn Profile (company_linkedin) • Company Website (company_website) Your requested columns: • LinkedIn Company Description (li_company_description) • Company Website Summary (website_text_summary) Total: 4 enrichments in 2 batches. OK to proceed? ``` **Pipeline mode** (user described a goal): 1. Pick the best matching recommended pipeline 2. Apply credit-discipline triggers (geography, size, department, age, posts) 3. Present the full pipeline with all batches 4. Show what's NOT enriched and why (credit-saving) ### Step 3 — confirm with user Before any enrichment call, show the plan and get explicit approval. For AI columns, show the prompt text or hand off to **mcp-classification** / **mcp-personalizer**. ### Step 4 — execute For each batch, call `mcp__leadspicker__enrich_contacts` once per column. Wait briefly between calls (≈1 s) to avoid rate-limits. ``` mcp__leadspicker__enrich_contacts( project_id=26256, magic_column_type="company_website", column_name="Company Website", ) ``` For an AI custom column: ``` mcp__leadspicker__enrich_contacts( project_id=26256, magic_column_type="ai_custom_column", column_name="Company Industry", prompt="Determine the primary industry of this company.\n\n…\n\nInput:\n- Company Name: {{company_name}}\n- Company Website: {{company_website}}\n- LinkedIn Company Description: {{linkedin_company_description}}\n- Company Website Summary: {{website_text_summary}}", is_boolean=False, ) ``` > Escape newlines in `prompt` as `\n` in the JSON payload that the MCP tool builds. > Template variables like `{{company_name}}` are filled by Leadspicker server-side. For pipelines where later batches genuinely need earlier data (e.g. `validate_emails` after `enrich_emails`, AI columns after enrichment), ask the user to confirm the previous batch completed in the Leadspicker UI before launching the next one. ### Step 5 — report summary After the batch finishes, report: - Columns created (mark prerequisites vs requested) - Any failures with reason - Suggested next steps: - After company data → "You can now run AI classification (mcp-classification)." - After email enrichment → "Validate emails next." - After LinkedIn person data → "Good base for icebreaker / personalization (mcp-personalizer)." --- ## Natural language → column matching Users won't always use exact column names. Map common phrases: | User says... | Column(s) | `magic_column_type` | | -------------------------------------------------- | -------------------------------------- | ------------------------------------------------------------------------ | | "company description", "popis firmy" | LinkedIn Company Description | `li_company_description` | | "website summary", "shrnutí webu", "co dělá firma" | Company Website Summary | `website_text_summary` | | "find emails", "najdi emaily" | Enrich Emails | `enrich_emails` | | "verify emails", "validuj emaily" | Validate Emails | `validate_emails` | | "LinkedIn data" | All LinkedIn person columns | (multiple) | | "company info", "firemní data" | All company data columns | (multiple) | | "job title", "pozice" | Position | `person_position` | | "headcount", "team size", "kolik lidí" | Department headcounts | `li_company_people_*` | | "everything", "full enrichment" | Full pipeline (warn about cost) | (multiple) | | "prepare for outreach", "připrav na outreach" | Lean Outreach Prep | (multiple) | | "company size", "velikost firmy" | LinkedIn Company Size + Employee Count | `li_company_size` (+ `li_company_employee_count` if user asks for exact) | | "posts", "příspěvky" | LinkedIn Latest Posts | `li_latest_posts` | | "bio", "about", "o sobě" | LinkedIn About Me | `li_about_me` | | "skills", "dovednosti" | LinkedIn Skills | `linkedin_skills` | | "industry", "odvětví" | Company Industry (AI) | `ai_custom_column` | | "icebreaker", "opener" | Icebreaker — Latest LI Posts (AI) | `ai_custom_column` | | "email host", "MX" | Email MX | `email_mx` | When in doubt, show the column list and ask the user to pick. --- ## Common errors | Symptom | Likely cause | Fix | | ------------------ | ------------------------------------------------------- | -------------------------------------------------------------------- | | Tool returns `404` | Wrong project | Re-list projects via `mcp__leadspicker__list_projects` and reconfirm | | Tool returns `400` | Invalid `magic_column_type` | Check the tables above for the exact value | | Tool returns `422` | Column with that name already exists, or invalid prompt | Pick a unique name or fix the prompt | | Tool returns `429` | Rate limit | Increase delay between calls to 2-3 s | --- ## Safety - **Always show the plan to the user** for approval before launching a batch. - For AI columns, **always show the prompt** before launching. - Default to lean enrichment; only add optional columns when explicitly triggered by user input.