# NetIntel Extract Table

> NetIntel Extract Table is a paid API for AI agents from netintel-production-440c.up.railway.app, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).

Extracts structured tabular data from a webpage or document URL, returning rows, columns, and a quality grade

## Facts

- Endpoint: POST https://netintel-production-440c.up.railway.app/extract/table
- Price: $0.05/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/netintel-extract-table-01d873e4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_s3W46oOc7g-C4lg8qdDtA

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability netintel-extract-table-01d873e4 -d '<json body>'
```

Example prompt: Can you extract the table from this product pricing page and give me the rows and columns as structured data? The URL is https://example.com/pricing.

## When to prefer this

Use this endpoint when you need to extract structured tabular data (rows and columns) from a specific web page URL and want a graded quality assessment of the extraction. Prefer this over generic scraping when you specifically need table structure in JSON format with column headers and per-row data.

## Known failure modes

- No table found on the page — returns empty rows/columns with low score
- URL is unreachable or returns non-200 status — likely returns error or empty result
- Page requires JavaScript rendering and table is dynamically loaded — may not be extracted
- Malformed URL input — likely returns validation error
- Rate limit or payment failure — 402 response if x402 payment not provided

## How this service works

Extract tabular data from messy text or HTML using Claude Haiku — detects columns and rows in unstructured content and returns clean structured JSON (columns + rows) so agents can turn pasted tables, HTML tables, and delimited text into usable data in one call.

## Output

Returns a JSON object with a quality grade (e.g. 'A') and numeric score (0-100), the extracted table containing column names, rows as key-value objects, row count, and column count, plus a list of findings/warnings and a cached flag indicating if the result was served from cache.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "properties": {
    "body": {
     "type": "object",
     "required": [
      "text"
     ],
     "properties": {
      "text": {
       "type": "string",
       "description": "The messy text or HTML containing tabular data to extract. Max 10000 words or 50KB. Aliases also accepted: content, html, csv, table, markdown, data."
      }
     }
    },
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "POST"
     ],
     "type": "string"
    },
    "bodyType": {
     "enum": [
      "json",
      "form-data",
      "text"
     ],
     "type": "string"
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object",
     "properties": {
      "grade": {
       "type": "string",
       "description": "Letter grade A-F"
      },
      "score": {
       "type": "number",
       "description": "Quality score 0-100"
      },
      "table": {
       "type": "object",
       "description": "Parsed table: columns (array of column names), rows (array of objects keyed by column name), and row_count (number of rows). Empty columns/rows with row_count 0 when no tabular structure is detected."
      },
      "cached": {
       "type": "boolean",
       "description": "True if served from the in-memory cache."
      },
      "findings": {
       "type": "array",
       "items": {
        "type": "string"
       },
       "description": "Informational findings (e.g. no_table_found, count_mismatch)."
      },
      "column_count": {
       "type": "number",
       "description": "Number of columns detected."
      }
     }
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "grade": "A",
  "score": 100,
  "table": {
   "rows": [
    {
     "qty": "10",
     "price": "5.00",
     "product": "Widget A"
    },
    {
     "qty": "4",
     "price": "7.50",
     "product": "Widget B"
    }
   ],
   "columns": [
    "product",
    "price",
    "qty"
   ],
   "row_count": 2
  },
  "cached": false,
  "findings": [],
  "column_count": 3
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/netintel-extract-table-01d873e4/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from netintel-production-440c.up.railway.app](https://www.zero.xyz/host/netintel-production-440c.up.railway.app/llms.txt)