> ## Documentation Index
> Fetch the complete documentation index at: https://docs.1eye.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Workflow examples

> Use 1eye MCP to investigate buyers, website activity, acquisition, and delivery.

These examples combine natural language prompts with the tool sequence needed to answer accurately. Replace example IDs with values returned by your workspace. The prompts are starting points; the assistant still needs to interpret dates and inspect the actual results.

## Find newly added buyers

```text theme={null}
Show up to 20 buyers added this week in America/Los_Angeles.
Include name, company, role, first signal type, and ICP/persona match.
State the exact dates and the total matching count.
```

Use `search_buyers` with `created_at` boundaries. Read `total` for the number of matching buyer records. To limit the query to buyers that currently have website signals, add `website_signal_count > 0`; to establish their initial source, inspect `first_signal_type` in the returned buyer records.

A buyer created this week may have an initial signal from an earlier date. If the question is about visits during the week, use the next workflow.

## Find buyers with website activity

```text theme={null}
Who visited our website this week in my workspace time zone?
Show distinct buyers, companies, visit counts, pages, and latest activity.
Separate newly created buyer records from buyers we already knew.
```

1. Call `get_workspace` to establish the workspace time zone.
2. Call `search_signals` with `signal_type = website` and `signal_at` date boundaries.
3. Continue through the required signal pages with the same filter.
4. Group signals by `buyer_id` and count the matching activity for each buyer.
5. Call `get_buyer` for each relevant buyer and inspect `created_at` to identify new records.

If the question is specifically about page visits, also add `trigger = visited_page`. Website signals can otherwise include form submissions and abandoned forms.

## Investigate visits from ads

```text theme={null}
Are we getting website visits from ad platforms this month?
Use visit-level attribution, UTM parameters, and ad click IDs.
Break down tracked visits by platform, list the identified buyers,
and distinguish paid ads from organic search and social referrals.
```

Search website signals for the period, then inspect:

| Evidence | Fields in the website signal |
| - | - |
| 1eye attribution | `website.attribution.type`, `source`, and `source_name` |
| Campaign tags | `website.utm.source`, `medium`, `campaign`, `term`, and `content` |
| Click identifiers | `website.click_ids`, including `gclid`, `gbraid`, `wbraid`, `dclid`, `li_fat_id`, `msclkid`, `fbclid`, `ttclid`, and `twclid` |

A source platform alone does not establish paid traffic: Google can be organic, and LinkedIn can be social. Report the attribution and evidence the signal actually contains. A click identifier is evidence to investigate, not a blanket guarantee of paid attribution for every platform.

Signal search has filters for UTM and click-ID values, but it does not expose a general “has click ID” or visit-attribution-type filter. Do not simulate an existence filter with an empty text value. For a complete platform breakdown, inspect the full matching set within a practical date range.

These results describe the website signals recorded by 1eye. Missing tags, unavailable identifiers, or direct attribution limit what can be concluded about the original acquisition source.

## Review a target list

```text theme={null}
List our target lists. For the list I choose, show its buyer count,
then show up to 10 matching buyers with verified email addresses.
```

Call `list_target_lists`, then `get_target_list` for the chosen ID. To read its buyers, use:

```json theme={null}
{
  "filter": {
    "match": "all",
    "rules": [
      { "field": "target_list_id", "operator": "eq", "value": "TARGET_LIST_ID" },
      { "field": "has_verified_email", "operator": "eq", "value": true }
    ]
  },
  "limit": 10
}
```

The current MCP tools read target lists and their buyers. Create lists or change list membership through the supported 1eye app workflow.

## Diagnose a destination

```text theme={null}
Show our active destinations and their connection status.
For the destination I choose, summarize delivery totals and show
the first 10 failed deliveries with their reasons.
```

Use `list_destinations`, `get_destination`, then `get_destination_console`:

```json theme={null}
{
  "destination_id": "DESTINATION_ID",
  "status": "failed",
  "limit": 10
}
```

The console returns one row per buyer's latest delivery status. Its `total`, `pushed`, `pending`, `failed`, and `removed` counts cover all buyers, regardless of the status filter or page. Read those counters for the summary and the returned rows for examples.

## Review credit use

```text theme={null}
Show October 2026 credit usage by day and activity.
Explain the main drivers and show the current credit costs.
Then retrieve the current billing plan and balance.
```

Call `get_usage` with `{"month":"2026-10"}` and `get_billing` with `{}`. Usage months are calendar months in UTC. Keep that boundary explicit when comparing usage with a report in another time zone.

Billing tools retrieve information. Purchasing credits and changing the plan are actions in the 1eye app.

## Prepare a configuration change

```text theme={null}
Retrieve our current Signal Settings. Prepare a change that caps
website capture at 50 new buyers per day. Show the exact proposed
arguments and ask me to confirm before applying it.
```

First call `get_signal_settings`. After reviewing the current settings and obtaining confirmation, the change arguments are:

```json theme={null}
{
  "website": {
    "daily_buyer_limit": 50
  }
}
```

Call `update_signal_settings` only after approval, then inspect its returned settings. Only supplied fields change. This cap concerns **new buyers per day**, not a maximum number of website visits.


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