pipeline-history

Pipeline History Analysis

When to Use

• Historical pipeline analysis (how deals progressed over time)
• Funnel conversion rates (what % of deals reached each stage)
• Stage duration analysis (how long deals stay in each stage)
• Deal progression timelines (when did a deal move from X to Y)

Workflow

All commands use execute-read-command. Do not pass --json in argv — JSON output is automatic.

Step 1: Identify the Status Field

execute-read-command(command: "field ls", argv: ["--list-id", "YOUR_LIST_NAME"])

Look for a dropdown field tracking deal stages (typically named "Status", "Stage", or "Pipeline Stage"). Note its fieldId (e.g., field-260415).

Step 2: Export Current State

execute-read-command(command: "list export", argv: ["YOUR_LIST_NAME", "--field", "Status", "--max-results", "50"])

JSON output key is data.rows. Each row contains listEntryId, entityId, entityName, and current field values.

Step 3: Estimate API Cost (REQUIRED)

execute-read-command(command: "field history-bulk", argv: ["<field-id>", "--list-id", "YOUR_LIST_NAME", "--all", "--strategy", "field", "--dry-run"])

--strategy field reads the field's whole history in a few calls (one per 10,000 changes) and keeps the rows for this list's entries. For a global field (dry run fieldKind: "global", e.g. a company field) it reads the field's history across the whole org and keeps this list's companies or people; if that holds more than 200,000 changes it stops with an error — then sample with --max-results (one call per company or person, at most 500 via MCP). A global field's rows include changes from before the company joined the list. Enriched fields have no history. Check estimatedApiCalls (the per-entry worst case) in the dry run before running it.

Step 4: Fetch History

execute-read-command(command: "field history-bulk", argv: ["<field-id>", "--list-id", "YOUR_LIST_NAME", "--max-results", "50"])

For all entries (after the dry run):