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TradepilotUSA Table Extract Text
x402
TradepilotUSA Table Extract Text is a paid API for AI agents from www.tradepilotusa.com, paid per call via x402, $0.25/call, status unknown (last checked 2026-10-02).
Extracts and structures tabular data from unstructured source text, creating markdown tables with ambiguity flags and evidence gaps
Price / call$0.25
Success—
StatusUnknown
How this service works
Extract tables present in supplied text into a structured representation; preserve values and flag ambiguous cells. Uses buyer-supplied information only; no external research or outbound actions. Returns a draft for human review.
Output
Returns a JSON object containing: a markdown-formatted table (or derived structured table if no explicit tables exist), a plain-language summary of what was found, assumptions made during extraction, evidence gaps where data was missing or unclear, a requires_human_review flag, provenance metadata (source, timestamp), and a request ID. Cells with ambiguous content are flagged inline.
Example use cases
Contract terms extraction
“I have a long contract document in plain text — can you pull out any tabular information, like pricing tiers, milestone dates, or party obligations, and format them as markdown tables? The brief is to extract all contractual terms and conditions, and the audience is our legal review team.”
Meeting notes to action table
“Take these meeting notes and extract any structured information into tables — things like who owns what tasks, deadlines, and priorities. The brief is to identify action items and owners. Source text: 'Alice will handle the client proposal by Friday. Bob owns the server migration, due end of month. Carol is reviewing the budget report, deadline Thursday.'”
Product spec sheet parsing
“Here's a block of product specification text — can you extract the specs into a clean table? Brief: identify all product attributes and their values. Audience: our e-commerce listing team. Source: 'Widget Pro: weight 2.3kg, dimensions 30x20x10cm, color options red/blue/black, warranty 2 years, SKU WP-2024.'”
Technical details
Endpoint
POSThttps://www.tradepilotusa.com/api/agent-commerce/v1/services/table_extract_text/execute?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to extract, identify, or structure tabular data from unstructured or narrative text — especially when the source may or may not contain explicit tables and you need a reliable structured output with transparency about ambiguity and gaps. Prefer this over generic LLM calls when you need provenance metadata, assumption tracking, and evidence gap reporting built into the response.
Side effects
- Source text too short (under 20 characters) returns validation error
- Source text exceeds 32,000 character limit causes rejection
- Brief shorter than 10 characters or exceeding 12,000 characters triggers validation error
- No tabular or structured data found in text — endpoint still returns a derived table with evidence_gaps noted
- Malformed JSON input returns HTTP 400
- Payment failure (insufficient USDC) returns HTTP 402
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| brief | string | |
| audience | string | |
| source_text | string |
Output
Schema · Response
{
"type": "json",
"example": {
"task": "table_extract_text",
"summary": "The supplied source text contains no explicit tables. A structured summary table was created to organize the narrative information by section, topic, and details. No ambiguous cells were found. Missin",
"artifact": "Illustrative excerpt from a synthetic provider check:\n## Extracted Tables from Source Text\n\n_No explicit tables were present in the supplied source text. However, the following structured representation is derived from the information given:_\n\n| Section | Topic | Details | Ambiguity Flag |\n|---------|--------------------------------|---------------------------------------------------------------------------------------------|----------------",
"provenance": {
"source": "buyer_supplied",
"generated_at": "2026-09-22T14:42:45.464Z",
"external_research_performed": false
},
"request_id": "example_table_extract_text",
"assumptions": [
"The source text does not contain any hidden or implied tables beyond the narrative sections.",
"The roles and scheduling information are accurately represented as stated."
],
"evidence_gaps": [
"No numeric or tabular data on appointment delays or project budgets.",
"No detailed consultation schedule or installation timeline tables."
],
"requires_human_review": true
}
}Tags
x402
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