API + MCP Guide

Subindex

Verified Research Engine · subindex.ai · Launch App

Subindex generates, validates, and updates research tables by synthesizing hundreds of calls to Perplexity and Claude. Give it a prompt or an existing table and it returns structured, verified answers across an entire research domain — not just a single query, but a complete field of questions answered at once.

What you want to do How Live example
Gather everything — survey a complete research domain at once Prompt → structured verified table 50+ Phase 3 oncology trials
Monitor anything — news, analyst projections, time-sensitive data Upload or generate → keep current Market info for 10 stocks
See everywhere — run the same questions across many entities One table, many subjects GenAI adoption across Fortune 500
How to Access
You want to… Use
Try it out or iron out your use case subindex.ai/app — web GUI for table validation and generation
Fact-check text or documents interactively subindex.ai/chex — web GUI for reference checks
Let an AI agent drive a workflow autonomously MCP server — install once, describe your task in plain English
One-off automation without writing code MCP server via Claude Code, Claude Desktop, or any MCP-compatible client
Run repeatable pipelines or batch jobs REST API + example scripts
Integrate into a product or SaaS REST API directly

GUI → API: The web GUIs are ideal for exploring and refining your use case. Once you know what you want, the MCP server or REST API is the better path — faster, repeatable, and fully automatable.

Table of Contents Get Your API Key

Get your API key at subindex.ai/account . New accounts receive $20 in free credits.

Download Examples

All scripts require Python 3.10+ and pip install requests .

Script Description Download
subindex_client.py Shared REST client (required by all examples) download
01_validate_table.py Validate an existing table download
02_generate_table.py Generate a table from a prompt download
03_update_table.py Re-run validation on a completed job download
04_reference_check.py Fact-check text or documents download

Or clone the full example set: 

# Download all examples at once
curl - O https://hyperplexity-storage.s3.amazonaws.com/website_downloads/examples/subindex_client.py \
     - O https://hyperplexity-storage.s3.amazonaws.com/website_downloads/examples/01_validate_table.py \
     - O https://hyperplexity-storage.s3.amazonaws.com/website_downloads/examples/02_generate_table.py \
     - O https://hyperplexity-storage.s3.amazonaws.com/website_downloads/examples/03_update_table.py \
     - O https://hyperplexity-storage.s3.amazonaws.com/website_downloads/examples/04_reference_check.py
pip install requests
export SUBINDEX_API_KEY=sbx_live_...
Quick Start: MCP

The MCP server lets any AI agent drive the full Subindex workflow autonomously — no scripting required.

Option A — Direct HTTP connection to Railway (recommended for Claude Code)

Connects directly to the hosted Subindex server over HTTP. No local install, no uvx , no package management — just one command.

Claude Code: 

claude mcp add subindex \
  --transport http \
  https://mcp-server-subindex-production.up.railway.app/ \
  --header "X-Api-Key: sbx_live_your_key_here"

Via config file ( .mcp.json in your repo root, or claude_desktop_config.json ): 

{
  "mcpServers": {
    "subindex": {
      "type": "http",
      "url": "https://mcp-server-subindex-production.up.railway.app/",
      "headers": {
        "X-Api-Key": "sbx_live_your_key_here"
      }
    }
  }
}

Why HTTP over uvx? The HTTP connection runs on Railway — always up to date, no local Python environment needed, and no version drift between the package you installed and the live server. Recommended for Claude Code and any project-level config.

Option B — Local install via uvx

Runs the server locally on your machine using uvx . Useful for Claude Desktop or offline/air-gapped environments.

Claude Code: 

claude mcp add subindex uvx mcp-server-subindex \
  - e SUBINDEX_API_KEY=sbx_live_your_key_here

Claude Desktop — add to claude_desktop_config.json :

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json 
{
  "mcpServers": {
    "subindex": {
      "command": "uvx",
      "args": ["mcp-server-subindex"],
      "env": {
        "SUBINDEX_API_KEY": "sbx_live_your_key_here"
      }
    }
  }
}

Project config (shared repo) — add .mcp.json to your repo root. Each person uses their own key; no key is committed to the repo: 

{
  "mcpServers": {
    "subindex": {
      "command": "uvx",
      "args": ["mcp-server-subindex"],
      "env": {
        "SUBINDEX_API_KEY": "${SUBINDEX_API_KEY}"
      }
    }
  }
}

OpenAI Codex CLI — add to your Codex config file ( ~/.codex/config.toml on macOS/Linux, %USERPROFILE%\.codex\config.toml on Windows): 

[mcp_servers.subindex]
command = "uvx"
args = ["mcp-server-subindex"]

[mcp_servers.subindex.env]
SUBINDEX_API_KEY = "sbx_live_your_key_here"

Then restart Codex and verify: 

codex mcp list
Option C — Smithery

Smithery is an MCP registry that works with Claude Code and other MCP-compatible clients including OpenClaw.

Step 1 — Install and log in: 

npx - y @smithery/cli@latest login
npx - y @smithery/cli@latest mcp add subindex/subindex --client claude-code

Step 2 — Authenticate with your API key:

Open your MCP client (e.g. Claude Code), go to /mcp , click subindex → Authenticate , and enter your Subindex API key in the Smithery page that opens.

Smithery login is a one-time step. You must log in before adding servers, or authentication will not be set up correctly.

What to Ask Your Agent

Once the MCP server is installed, describe your task in plain English. The agent drives the full workflow, pausing only when your input is genuinely needed.

Validate a table:

"Validate companies.xlsx using Subindex. Interview me about what each column means, then run the preview. If the results look good, approve the full validation."

Generate a table:

"Use Subindex to generate a table of the top 20 US hedge funds with columns: fund name, AUM, primary strategy, founding year, and HQ city. Approve the full validation when the preview looks right."

Re-run validation on the same table:

"Re-run update_table on job session_20260217_103045_abc123 to get an updated validation pass."

Fact-check a document:

"Use Subindex to fact-check this analyst report." (paste the text or share the file path)

Workflows 1. Validate an Existing Table

Minimum rows: Subindex is designed for tables with 4 or more data rows . Fewer rows may produce low-quality results.

Full flow: upload → interview → preview → refine → approve → download 

upload_file(filename, file_type, file_size [, file_path])   ← omit file_path on HTTP/Railway transport; run returned curl_command instead
  → start_table_validation(session_id, s3_key, filename)
      ┌── match found (score ≥ 0.85) → [preview auto-queued; response has preview_queued=true + job_id]
      └── no match → interview auto-started
            → wait_for_conversation / poll get_conversation
              → send_conversation_reply  (if AI asks questions)
              → [interview complete → preview auto-queued]

  → wait_for_job(job_id or session_id)  ← blocks until preview_complete
      → [optional] refine_table(conv_id, session_id, instructions)
      → approve_validation(job_id, cost_usd)
      → wait_for_job(job_id)            ← blocks until completed
      → get_results(job_id)

Key behavior: The preview is always auto-queued — after the interview finishes ( trigger_config_generation=true ), or when a config match is found ( match_score ≥ 0.85 , response includes preview_queued: true and job_id ). Call wait_for_job(session_id) directly in all cases (see Config reuse ).

Upload interview auto-approval: The interview may auto-approve in a single turn. If the conversation response has user_reply_needed: false and status: approved , proceed to wait_for_job(session_id) immediately — no reply is needed, even if the AI's message appears to ask for confirmation.

Skip the interview with instructions (fire-and-forget config generation):

Pass instructions to start_table_validation to bypass the interactive interview. The AI reads the table structure + your instructions and generates a config directly, then auto-triggers the preview — no clarifying questions needed. 

start_table_validation(session_id, s3_key, filename,
  instructions="This table lists hedge funds. Validate AUM, strategy, and HQ city. Use Bloomberg and SEC filings.")
  → response includes instructions_mode=true
  → wait_for_job(session_id)          ← config generation + preview tracked automatically
  → approve_validation(job_id, cost_usd)
  → wait_for_job(job_id)
  → get_results(job_id)

Cost gate: Config generation and the 3-row preview are free . Full validation is charged at approve_validation — you always see the cost estimate at preview_complete before anything is billed. If your balance is insufficient, approve_validation returns an insufficient_balance error with the required amount.

Refine the table before approving by calling refine_table . The classifier reads your natural-language instruction and routes to one of:

Capability Example
Adjust validation approach for existing columns "Use SEC filings as the primary source for revenue. Require exact match for ticker symbols."
Add or remove columns "Add a column for HQ city. Remove the revenue column."
Add or remove rows by name "Add California and Texas as rows." / "Remove rows 3 and 7."
Discover additional rows by description (early beta) "Find 10 more biotech companies similar to these." 
refine_table(conversation_id, session_id,
  "Use SEC filings as the primary source for revenue. Require exact match for ticker symbols.")

A new preview runs automatically after refinement.

Beta caveat: column adds/removes and row removes by name are reliable. Row discovery (finding additional similar rows by description) and complex multi-intent edits may need retries — try a more specific instruction, or fall back to a fresh start_table_validation run.

Adding rows or columns AFTER full validation completes? Use update_table , not refine_table . refine_table operates on the preview before approval; update_table starts from your validated data, so you keep your existing results.

Python script: examples/01_validate_table.py 

export SUBINDEX_API_KEY=sbx_live_...
python examples/01_validate_table.py companies.xlsx
python examples/01_validate_table.py companies.xlsx --refine "Add LinkedIn URL column"

 Fire-and-forget: provide instructions to skip the interview entirely
python examples/01_validate_table.py companies.xlsx \
    --instructions "This table lists hedge funds. Validate AUM, strategy, and HQ city."

Which mode to use: Always try file_path first. If the server can read the file (stdio/uvx transport), upload completes in one step. If you get "File not found" (HTTP/Railway transport — server is remote), call again without file_path to receive a presigned S3 URL and a ready-to-run curl_command . The two-step path requires shell access to run curl; Claude Desktop users should use the uvx transport so file_path works.

2. Generate a Table from a Prompt

Describe the table you want — rows, columns, scope — and Subindex builds and validates it from scratch. Designed for tables with 4 or more rows .

Literature / academic tables: if your prompt is about scientific papers, a research domain, or asks for a literature review, Subindex automatically routes through a dedicated literature pipeline. It builds a structured response from the relevant academic literature, then digs deep into each surfaced paper to populate standard publication fields (title, authors, journal, date, abstract, URL) alongside any custom columns you define in the conversation. No separate tool — start_table_maker detects the literature intent and forks internally. 

start_table_maker("Top 20 US biotech companies: name, ticker, market cap, lead drug, phase")
  → wait_for_conversation / poll get_conversation
    → send_conversation_reply  (if AI asks clarifying questions)
    → [table builds → preview auto-queued]

  → wait_for_job(session_id)          ← spans table-maker + preview phases
    → approve_validation(job_id, cost_usd)
    → wait_for_job(job_id)
    → get_results(job_id)

Auto-approve: The agent can auto-approve the preview and proceed to full validation without human intervention. The preview table is included inline in the preview_complete response.

Cost: ~$0.05/cell (standard), up to ~$0.25/cell (advanced). $2 minimum per run.

Skip confirmation with auto_start=True (fire-and-forget generation):

Pass auto_start=True to skip the AI's clarifying questions and structure-confirmation step. The AI generates the table immediately from the message alone. Use when your message fully describes the desired table. 

start_table_maker(
  "Top 20 US hedge funds: fund name, AUM, primary strategy, founding year, HQ city",
  auto_start=True)
  → wait_for_conversation(conversation_id, session_id)
      ← returns trigger_execution=true on first response (no Q&A)
  → wait_for_job(session_id)          ← table building + preview
  → approve_validation(job_id, cost_usd)
  → wait_for_job(job_id)
  → get_results(job_id)

Why wait_for_conversation with auto_start=True ? Even though there is no Q&A, wait_for_conversation is still required — it returns trigger_execution: true in a single blocking call (no reply needed), signaling that the table-maker has started. Calling wait_for_job before this call returns would be premature, as the table-maker may not have been triggered yet.

Cost gate: Table building and the 3-row preview are free . Full validation is charged at approve_validation — you always see the cost estimate at preview_complete before anything is billed. If your balance is insufficient, approve_validation returns an insufficient_balance error with the required amount.

Python script: examples/02_generate_table.py 

python examples/02_generate_table.py "Top 10 US hedge funds: fund name, AUM, strategy, HQ city"
python examples/02_generate_table.py --prompt-file my_spec.txt

 Fire-and-forget: skip clarifying Q&A and generate immediately from the prompt
python examples/02_generate_table.py --auto-start "Top 10 US hedge funds: fund name, AUM, strategy, HQ city"
3. Update a Table (Re-run Validation Pass)

Re-run validation on a completed job — no re-upload or manual edits needed. The table iterates automatically, re-validating the same data with the same config to pick up any changes in source data.

update_table is also the right path for adding rows or columns AFTER full validation completes: it starts from your validated data, so existing results are preserved. Use refine_table only on the preview before approval; once a run is completed , switch to update_table .

If you want to incorporate manual edits to the output file, re-upload the edited file via upload_file + start_table_validation — a matching config will be found automatically (score ≥ 0.85).

Pricing note: today, an update_table run charges as a fresh validation pass on every row, not just newly added rows. Per-row pricing for incremental additions is on the roadmap. 

update_table(source_job_id)           ← re-validates existing enriched output
  → wait_for_job(new_job_id)          ← blocks until preview_complete
    → approve_validation(new_job_id, cost_usd)
    → wait_for_job(new_job_id)
    → get_results(new_job_id)

Python script: examples/03_update_table.py 

python examples/03_update_table.py session_20260217_103045_abc123
python examples/03_update_table.py session_20260217_103045_abc123 --version 2
4. Fact-Check Text or Documents (Chex)

Submit any text, report, or document. Subindex checks each factual claim against authoritative sources and returns the same output format as standard table validation: an Excel (XLSX) file, an interactive viewer URL, and a metadata JSON.

Minimum claims: Subindex is designed for text with 4 or more factual claims . Fewer claims may produce low-quality results. 

start_reference_check(text="...")           ← inline text (or auto_approve=True to skip the gate)
  or
upload_file(filename, "pdf", file_size [, file_path])   ← upload PDF/document first
  → start_reference_check(s3_key=s3_key)

→ wait_for_job(job_id)                ← spans extraction + 3-row preview; stops at preview_complete
  → preview_table (3 validated sample claims) + cost_estimate shown in response
  → approve_validation(job_id, approved_cost_usd=X)   ← triggers Phase 2
  → wait_for_job(job_id)              ← waits for completed
  → get_results(job_id)               ← download_url (XLSX) + interactive_viewer_url + metadata_url

Three-phase flow: Phase 1 (claim extraction, free) runs automatically, then a 3-row preview validates sample claims (free, auto-triggered). Both phases are tracked by a single wait_for_job call that stops at status=preview_complete . Review preview_table (3 validated sample claims with support level and citations) and cost_estimate , then call approve_validation to start Phase 2 (full validation, charged). Pass auto_approve=True to skip the gate and run straight through to completed .

Progress tracking: get_job_messages returns empty during claim extraction (Phase 1). Progress messages are available during the preview validation phase. wait_for_job handles both phases automatically.

Output: Excel (XLSX) file with per-claim rows. Support levels: SUPPORTED / PARTIAL / UNSUPPORTED / UNVERIFIABLE. Share interactive_viewer_url with human stakeholders — it renders sources and confidence scores in a clean UI.

Python script: examples/04_reference_check.py | Sample output: sample_outputs/reference_check_output.json 

# Fact-check inline text
python examples/04_reference_check.py --text "Bitcoin was created by Satoshi Nakamoto in 2009."

# Fact-check a PDF
python examples/04_reference_check.py --file analyst_report.pdf

# Fact-check multiple documents concatenated
cat doc1.txt doc2.txt | python examples/04_reference_check.py --stdin

--stdin: Concatenates all piped content as a single inline text payload. All claims are attributed to the combined document.

Environment Variables
Variable Description
SUBINDEX_API_KEY API key from subindex.ai/account . Required. New accounts get $20 free.
SUBINDEX_API_URL Override the API base URL (useful for dev/staging environments).
Direct REST API

All tools in the MCP server are thin wrappers over the REST API. You can call it directly from any language.

Base URL: https://api.subindex.ai/v1

Auth: Authorization: Bearer sbx_live_your_key_here

Response envelope: 

{
  "success": true,
  "data": { ... },
  "meta": { "request_id": "...", "timestamp": "..." }
}
Python client (minimal) 
import os, requests

BASE_URL = "https://api.subindex.ai/v1"
HEADERS  = {"Authorization": f"Bearer {os.environ['SUBINDEX_API_KEY']}"}

def api_get(path, **kwargs):
    r = requests.get(f"{BASE_URL}{path}", headers=HEADERS, **kwargs)
    r.raise_for_status()
    return r.json()["data"]

def api_post(path, **kwargs):
    r = requests.post(f"{BASE_URL}{path}", headers=HEADERS, **kwargs)
    r.raise_for_status()
    return r.json()["data"]

A full standalone client module is in examples/subindex_client.py .

API Endpoint Reference Uploads
Method Path Description
POST /uploads/presigned Get a presigned S3 URL to upload a file
PUT <presigned_url> Upload file bytes directly to S3 (no auth header)
POST /uploads/confirm Confirm upload; detect config matches; auto-start interview if no match

Presigned upload request: 

{
  "filename": "companies.xlsx",
  "file_size": 2048000,
  "file_type": "excel",
  "content_type": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
}

Content types: excel → .xlsx , csv → .csv , pdf → .pdf

Confirm upload request (optional fields): 

{
  "session_id": "session_20260305_...",
  "s3_key": "results/.../file.xlsx",
  "filename": "companies.xlsx",
  "instructions": "Validate AUM, strategy, and HQ city. Use Bloomberg and SEC filings as sources.",
  "config_id": "session_20260217_103045_abc123_config_v1_..."
}

instructions — if provided, bypasses the interactive upload interview. The AI generates the config directly from the table structure + instructions. Response includes instructions_mode: true and conversation_id . Use wait_for_job(session_id) to track progress — do NOT poll the conversation.

config_id — if provided, skips matching and the interview entirely. The specified config is applied immediately and the preview is auto-queued. Response includes preview_queued: true and job_id . Use wait_for_job(job_id) to track progress. The configuration_id for any completed job is returned by GET /jobs/{id}/results under job_info.configuration_id .

Conversations
Method Path Description
POST /conversations/table-maker Start a Table Maker session with a natural language prompt
GET /conversations/{id}?session_id= Poll conversation for status / AI messages
POST /conversations/{id}/message Send a reply to the AI
POST /conversations/{id}/refine-table Refine the table — adjust validation, add/remove columns, add/remove rows

Table Maker request body: 

{
  "message": "Top 20 US hedge funds: fund name, AUM, primary strategy, founding year, HQ city",
  "auto_start": true
}

auto_start — if true , the AI skips clarifying questions and the structure-confirmation step, proceeding directly to table generation. The first get_conversation response will have trigger_execution: true . Use when your message fully describes the desired table.

Jobs
Method Path Description
GET /jobs/{id} Get job status and progress
GET /jobs/{id}/messages Fetch live progress messages (paginated by since_seq )
POST /jobs/{id}/validate Approve full validation — credits charged here
GET /jobs/{id}/results Fetch download URL, metadata, viewer URL
POST /jobs/update-table Re-validate enriched output after corrections
POST /jobs/reference-check Submit text or file for claim verification

Job status values:

Status Meaning
queued Accepted, waiting to start
processing Actively running
preview_complete Free preview done — review results and approve full run
completed Full validation complete, results ready
failed Error — check error.message
Account
Method Path Description
GET /account/balance Current credit balance and this-month usage
GET /account/usage Billing history (supports start_date , end_date , limit , offset )
MCP Prompts

Three built-in prompts act as workflow starters — select them from the prompt picker in your MCP client (Claude Code: / menu; Claude Desktop: the prompt icon) and fill in the arguments.

Prompt Arguments What it does
generate_table description (required), columns (optional) Builds a step-by-step instruction for creating a new research table from a natural language description
validate_file file_path (required), instructions (optional) Generates the full validation workflow for an existing Excel or CSV file
fact_check_text text (required) Generates the reference-check workflow for fact-checking a text passage
MCP Tool Reference

Every tool response includes a _guidance block with a plain-English summary and the exact next tool call(s) — enabling fully autonomous agent workflows.

Tool Description
upload_file Upload Excel, CSV, or PDF. Try with file_path first (one step, stdio/uvx). If the server can't read the local path, call again without file_path to get a presigned S3 URL + curl command (HTTP/Railway transport — requires shell to run curl).
start_table_validation Confirm upload; detect config matches; auto-start interview if needed
start_table_maker Start an AI conversation to generate a table from a prompt
get_conversation Poll a conversation for AI responses or status changes
send_conversation_reply Reply to AI questions during an interview or table-maker session
wait_for_conversation Block until conversation needs input or finishes (emits live progress)
refine_table Refine the table with natural-language instructions: adjust validation approach (sources, strictness), add/remove columns, or add/remove rows. Row-level edits are in early beta.
wait_for_job Block until preview_complete , completed , or failed (preferred progress tracker)
get_job_status One-shot status poll
get_job_messages Fetch progress messages with native percentages (paginated)
approve_validation Approve preview → start full validation (credits charged here)
get_results Download URL, inline metadata, interactive viewer URL
update_table Re-validate enriched output after analyst corrections
start_reference_check Submit text or file for claim and citation verification
get_balance Check credit balance
get_usage Review billing history
Key Behaviors Auto-queued preview

The preview is automatically queued in all three paths after start_table_validation :

Path Trigger What to call next
Config match (score ≥ 0.85) preview_queued: true in response wait_for_job(job_id)
instructions= provided instructions_mode: true in response wait_for_job(session_id)
Interview ran trigger_config_generation=true from conversation wait_for_job(session_id)

To reuse a config from a different session, pass config_id to start_table_validation — the preview will be auto-queued immediately.

Config reuse

If start_table_validation returns match_score ≥ 0.85 , the preview is automatically queued using the matched config. The response includes preview_queued: true and job_id — call wait_for_job(job_id) directly, no interview needed.

The configuration_id from any completed job's get_results response can be reused on future uploads of similar tables.

Cost confirmation gate

approve_validation requires approved_cost_usd matching the preview estimate. This prevents surprise charges. The estimate is in the preview_complete job status response under cost_estimate.estimated_total_cost_usd .

This gate applies regardless of whether instructions or auto_start was used — both only skip the interview/confirmation conversation , not the cost approval step. If your balance is insufficient when approve_validation is called, the API returns: 

{ "error": "insufficient_balance", "required_usd": 4.20, "current_balance_usd": 1.50 }
Fire-and-forget shortcuts

Two optional flags let fully automated pipelines skip interactive steps:

Flag Tool Skips Next step
instructions="..." start_table_validation Upload interview Q&A wait_for_job(session_id)
auto_start=True start_table_maker Structure confirmation wait_for_conversation → wait_for_job

These flags use different terminal signals: instructions= (a config-gen flow) causes trigger_config_generation: true on the conversation response; auto_start=True (a table-maker flow) causes trigger_execution: true . Both skip interactive Q&A but produce different fields — do not wait for trigger_execution when using the instructions= upload path. The preview_complete cost gate and approve_validation still apply.

Consuming results: humans vs AI agents

Output files generated per run:

File Format Description
Preview table Markdown (inline) First 3 rows as markdown text; returned inline in the preview_complete job status response (not a separate download). Also available in metadata.json under markdown_table .
Enriched results Excel ( .xlsx ) Ideal for sharing with humans; sources and citations are embedded in cell comments
Full metadata metadata.json Complete per-cell detail for every row; use the row_key field to drill into specific rows programmatically

get_results returns:

Field Type Best for
results.interactive_viewer_url URL Humans — web viewer with confidence indicators (requires login at subindex.ai with the same email as your API key)
results.download_url Presigned URL Humans — download the enriched Excel (.xlsx) directly
results.metadata_url Presigned URL AI agents — JSON file with all rows, per-cell details, and source citations

Recommended AI agent workflow:

  1. At preview_complete : read the inline preview_table (markdown, 3 rows) from GET /jobs/{id} to survey the table structure and spot-check values. The AI agent can review this inline table and call approve_validation directly — no human approval step is required.
  2. After full validation: fetch results.metadata_url → table_metadata.json . This contains every validated row.
  3. Use rows[].row_key (stable SHA-256) to cross-reference rows between the markdown summary and the detailed JSON.
  4. Per-cell fields in table_metadata.json :
    • cells[col].value — validated value (legacy files may use full_value )
    • cells[col].confidence — HIGH / MEDIUM / LOW / ID
    • cells[col].comment.validator_explanation — reasoning
    • cells[col].comment.key_citation — top authoritative source
    • cells[col].comment.sources[] — all sources with url and snippet
Pricing
Mode Cost
Preview (first 3 rows) Free
Standard validation ~$0.05 / cell
Advanced validation up to ~$0.25 / cell
Minimum per run $2.00
Reference check TBD — contact support

Credits are prepaid. Get $20 free at subindex.ai/account .

Standard validation is used for most tables. Advanced validation is selected automatically when the table requires more sophisticated reasoning (e.g., scientific data, complex financial metrics, or cells with high ambiguity).

Links