hypertab API Documentation
The active table where AI agents operate and humans supervise. Columns don't just hold data, they DO things.
hypertab://tables to see your workspace, and start operating. Every response includes a hints array with the recommended next step.1. Get your API key
Sign up and open the dashboard MCP page at app.hypertab.ai. It generates a revealed setup key, a full .mcp.json block, and a chat-ready install prompt for your AI agent. Keys start with ht_sk_ and authenticate both REST API and MCP connections.
ht_sk_your_api_key_here2. Install Hypertab in your agent
Run npx @hypertabai/mcp install once. It writes the MCP config to the right location for your agent and installs the Hypertab skill where supported. No JSON editing, no skill-file hunting, one command and a restart.
Pick your agent on the right. The command prints what it wrote and what to do next.
Prefer env vars on shared machines? Use HYPERTAB_API_KEY=... as a prefix instead of --api-key. Skill file: https://hypertab.ai/skill.md
npx @hypertabai/mcp install --client claude-code --scope project --api-key ht_sk_YOUR_KEY3. Create a table
Tables are the core data structure. Each table has columns (static or smart) and rows. Table and column names must be lowercase with underscores.
{
"tool": "hypertab_create_table",
"arguments": {
"name": "leads",
"columns": [
{ "name": "company", "type": "text" },
{ "name": "website", "type": "url" },
{ "name": "size", "type": "number" }
]
}
}4. Insert rows
Insert up to 10,000 rows per call. Column names are fuzzy-matched, if you type companey instead of company, hypertab auto-corrects it and tells you in the response.
{
"tool": "hypertab_insert_rows",
"arguments": {
"table": "leads",
"rows": [
{ "company": "Acme Corp", "website": "https://acme.com", "size": 500 },
{ "company": "Globex", "website": "https://globex.com", "size": 1200 }
]
}
}5. Add a smart column
Smart columns process data per row. Use {{column_name}} to reference other columns as input. This HTTP column calls Gemini to classify each lead.
{
"tool": "hypertab_add_smart_column",
"arguments": {
"table": "leads",
"name": "industry",
"type": "text",
"kind": "http",
"config": {
"url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=YOUR_KEY",
"method": "POST",
"headers": { "Content-Type": "application/json" },
"body": {
"contents": [{ "parts": [{ "text": "What industry is {{company}} ({{website}})? Reply with one word." }] }]
},
"extract": "candidates[0].content.parts[0].text",
"run_condition": "{{company}} IS NOT EMPTY"
},
"rate_limit": "conservative"
}
}6. Run the smart column
After adding a smart column, trigger it to process all existing rows. New rows will be processed automatically if auto_run is enabled.
{
"tool": "hypertab_run_column",
"arguments": {
"table": "leads",
"column": "industry"
}
}7. Check progress
Monitor the run status to see how many rows have been processed. The response includes total rows, processed count, error count, and completion percentage.
{
"tool": "hypertab_get_column_run_status",
"arguments": {
"run_id": "run_abc123"
}
}8. Query results
Once complete, query the table to see the smart column values populated for each row.
{
"tool": "hypertab_query_rows",
"arguments": {
"table": "leads",
"columns": ["company", "website", "size", "industry"],
"limit": 10
}
}