Stackby

@Stackby list all rows in the 'Q1 Campaign Leads' table where Status is 'Qualified', sorted by Score descending

Retrieved 23 rows from the Q1 Campaign Leads table matching Status='Qualified'. Top entries include: Lead #4729 (Score: 94, Company: Acme Corp, Contact: Sarah Chen), Lead #4801 (Score: 89, Company: BuildRight Inc, Contact: James Park), Lead #4756 (Score: 87, Company: DataFlow Systems, Contact: Maria Lopez). All rows include fields for Lead ID, Company, Contact, Email, Score, Status, and Last Contacted date. This example shows one page of results; pagination would be required for larger result sets.

This read-heavy example demonstrates filtering and sorting capabilities. Users must first call List Workspaces → List Stacks → List Tables → List Columns to discover the correct table ID and field names. Stackby's API returns one page at a time; large tables require multiple requests. View-based filtering (via List Views) can pre-filter results server-side to reduce payload size.

@Stackby create three new rows in the 'Product Roadmap' table: Feature A (Priority: High, Quarter: Q2), Feature B (Priority: Medium, Quarter: Q3), Feature C (Priority: Low, Quarter: Q2)

Successfully created 3 rows in the Product Roadmap table. New row IDs: rec8Kx2mP9, rec7Nq4sL1, rec5Yt8wR3. Each row was populated with the specified Priority and Quarter values; other fields (Owner, Status, Description) were left empty and can be updated separately. This example illustrates batch creation—up to 10 rows can be written in a single request, reducing API call overhead for bulk imports or multi-item planning sessions.

Write operations require correct field names (obtained via List Columns) and respect Stackby's field type constraints (text, number, single-select, etc.). The MCP supports creating up to 10 rows per request; larger batches need multiple calls. API key must have write permissions on the target stack. Changes are immediate and irreversible without a separate update or delete call.

@Stackby pull all rows from the 'Sprint Backlog' table where Assignee is 'Jordan' and Status is not 'Done', then draft a standup summary grouped by priority

Found 8 open tasks assigned to Jordan in the Sprint Backlog. High Priority (3 tasks): Implement OAuth flow, Fix payment gateway bug, Update API documentation. Medium Priority (4 tasks): Refactor user service, Add unit tests for auth module, Update deployment scripts, Review PR #847. Low Priority (1 task): Update README with new endpoints. Jordan's workload is weighted toward high-priority items; consider redistributing medium-priority tasks if capacity is tight. This synthesis example shows how the MCP's row data feeds into AI-driven summarization and planning.

This example pairs Stackby's List Rows filtering with the AI's reasoning to produce actionable insights. The MCP returns raw row data; the AI interprets field values (Priority, Status, Assignee) and generates the summary. Users should verify field names match their schema. For real-time standup prep, combine this with a saved view in Stackby to pre-filter rows and reduce query complexity.

When Stackby beats a spreadsheet for support triage

A 6-person support team logs tickets in Stackby with custom views per agent and a shared triage table. The MCP's List Views and List Rows tools let an AI assistant pull the day's unassigned tickets during standup, filter by priority, and route them to the right agent by creating rows in their personal queue. This works because Stackby's view system mirrors how the team already thinks about ownership. The trade-off: if your ticket volume exceeds 500 new rows per day, the single-field sorting and 10-row batch limit start to slow down bulk operations. Below that threshold, the MCP turns Stackby into a lightweight ticketing system without forcing a migration to Zendesk. If your team already lives in Stackby for other workflows, this is the fastest path to AI-assisted triage.

Why solo consultants pick Stackby for client intake

A solo consultant tracks 15-20 active client projects in a Stackby stack with tables for leads, contracts, and deliverables. The MCP's Create Rows and List Columns tools let an AI assistant log new inquiries from email, auto-populate contract templates, and update project status without opening the Stackby UI. This scenario wins because the consultant needs structured data but doesn't want the overhead of a CRM. The API key auth means no OAuth dance, and the 9-tool scope covers the full CRUD cycle. The limit: if the consultant scales to a 3-person agency, Stackby's workspace permissions get clunky and the MCP's List Workspaces call becomes a bottleneck. For a one-person shop managing under 100 active rows across tables, this MCP delivers CRM-lite automation at spreadsheet simplicity.

When Stackby's views solve multi-vendor event planning

A 4-person event planning team coordinates 8 vendors per event using a Stackby table with vendor-specific views and a master checklist. The MCP's List Views and Delete Rows tools let an AI assistant pull each vendor's outstanding tasks during weekly check-ins, mark completed items, and archive closed events by deleting rows in bulk. This works because Stackby's view filtering matches how the team segments vendor responsibilities, and the 10-row delete limit aligns with typical weekly task volume. The boundary: if an event grows beyond 12 vendors or the checklist exceeds 200 line items, the single-page List Rows call forces pagination that slows down real-time lookups. For mid-sized events with clear vendor boundaries, the MCP turns Stackby into a shared source of truth that an AI can query and update without manual data entry.