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---
name: leadmagic-tables
description: Research and enrich B2B accounts and contacts, clean LeadMagic Tables, and use connected CRM integrations through the LeadMagic MCP tools.
---

# LeadMagic Tables and enrichment

Use the connected LeadMagic MCP server for structured B2B data and workspace actions. Tables is a GTM workspace: rows can combine research, enrichment, formulas, AI, and supported CRM integrations. Tool availability follows the authenticated workspace's permissions and enabled features.

## Account and contact research

- Use `account_intel` for a company briefing, `research_account` for a lighter snapshot, and `enrich_company` for firmographics. Prefer a known company domain over an ambiguous company name.
- Use `enrich_contact` for a contact dossier and `find_decision_makers` for leadership. To find who holds one role or title at a company, call `find_people_by_role` first (2 credits if found, free on a miss); if it finds no one, report the miss rather than answering from people search. In a table, use the `role_finder` column for the same ask. Search returns discovery records; request email or mobile enrichment only when the user asks for that channel.
- `check_credit_balance` identifies the connected account/workspace and available credits. Use the free `preview_cost` before paid research or batches, and keep the scope within the user's request. A request for a cost estimate never authorizes the paid tool. Missing, unavailable, failed, and not-found results are different; preserve them.

## Inspect and clean a table

1. Resolve the table with `list_sheets`, then call `get_sheet`. Use the returned sheet id, row ids, and column keys; display names are not column keys.
2. Fetch `get_sheet_stats` for missing, unique, duplicate, and run-state counts. Before asserting whole-table missing or duplicate conclusions, use `read_sheet_rows` and continue pagination until every row is read. Verify the row count, compute missing and duplicate counts from the rows, state the matching rule, and compare them with the statistics. Do not infer whole-table conclusions from a sample or statistics alone. If pagination is incomplete or a read fails, state the inspected scope and leave whole-table counts unverified. Validate that returned counters are possible: empty counts cannot be negative, filled counts cannot exceed totals, and percentages cannot exceed 100%. Treat impossible statistics as unavailable and report only values verified from row reads.
3. Make cell updates only when the user has explicitly authorized the table, affected rows/columns, and transformation. Use `set_sheet_cells` in batches. For repeatable transformations add a formula column using `add_sheet_columns` or `configure_sheet_column` only within the authorized scope. A request to inspect or review data authorizes reads only. Preserve original columns unless the user asked to replace them. Do not merge distinct employment records or discard alternate contact values as duplicates without a stated matching rule.
4. For requested removal, inspect affected row ids and honor the confirmation returned by `delete_sheet_rows`. Rows and columns have restore tools. Cleaning alone does not authorize deleting an entire table or sending its data elsewhere.
5. Re-read rows and statistics to verify the actual result. Apply the same validity checks to returned statistics; if counters are impossible, mark them unavailable and rely only on verified row reads. Report changed and unresolved counts separately.

## Enrich account lists

Inspect the existing columns before adding more. Map company or contact inputs from known column keys into enrichment columns. `add_sheet_rows` and `add_sheet_columns` add/configure data but do not fill enrichment results by themselves.

Every paid `run_sheet_column` execution requires a quote and human approval, including a small sample. Choose the exact sheet, column keys (including `also_column_keys` for dependent columns), row scope, and any sa

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