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

# score

> Score a lead against your live ICP model — layered Fit · Pain · Intent · Ability plus an enrichment worklist. Works on accounts in Nous and on cold leads that aren't.

Get your workspace's own ICP judgment on a lead: a 0–100 fit + tier, a decaying intent score, and the **layered** breakdown — **Fit · Pain · Intent · Ability** — where any layer you have no evidence for comes back **unknown, never 0**, with a `missing` worklist telling you what to enrich next.

Three ways to call it:

* **A lead in Nous** — pass an `identifier`. Scored off its claims and staked; keeps evolving.
* **One cold lead not in Nous** — pass `attributes`. Scored inline; nothing is written.
* **A whole external list** — pass `leads` (up to 1000). Scored inline in one call; nothing written.

## Parameters

| Name          | Type   | Required | Description                                                                                                                                                      |
| ------------- | ------ | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `identifier`  | string |          | One lead in the graph — email, domain, LinkedIn URL, or entity UUID                                                                                              |
| `attributes`  | object |          | A **cold lead not in Nous** — `{ job_title, seniority, company_type, industry, employee_count, keywords or headline, location }`. Scored inline; nothing written |
| `leads`       | array  |          | An **external list** (up to 1000) — each an `attributes` object, optionally with a `ref` echoed back. Scored inline; nothing written                             |
| `identifiers` | array  |          | A batch of graph leads (max 100); loop for larger lists                                                                                                          |
| `intent`      | string |          | Optional hint about why you're scoring (recorded, does not change the score)                                                                                     |

## Returns

A known or cold lead — the fit, intent, and the layered read with its worklist:

```
sarah@acme.com — ICP 88/100 (tier 1) ✓fit · intent 0/100 Dormant
    layers: fit 88
    unknown: pain, intent, ability — research — strong fit; enrich pain and intent before outreach
    Scorecard: 3 signals fired — revenue_owner, b2b_saas, …
```

A cold lead scored inline is the same, tagged `scored inline (not in the graph)`.

An **external list** returns a summary — how many scored and the tier spread — while the raw per-lead results (each with its `ref`, `icp`, and `layered`) come back over the API:

```
Scored 812/1000 inline (nothing written). Tiers: tier 2 401 · tier 3 288 · tier 1 96 · not icp 27
```

## The layered model

The score is four independent layers, each resolved on its own evidence — **Fit** (who they are), **Pain** (do they have the problem), **Intent** (is it the right time), **Ability** (can they buy). A LinkedIn headline resolves **Fit** and leaves the rest **unknown** (never 0); `missing` is the worklist, and `play` is the recommended action (`research` · `nurture` · `work now` · `enrich` · `suppress`). As enrichment lands the layers light up. Tune the model on the **ICP** page or with [`build_icp_model`](/mcp/tools/build-icp-model).

## When to call it

* **Triage a cold list** before enrichment — `leads`, nothing written.
* **Score one prospect** from a LinkedIn scrape — `attributes`.
* **Get the live number** on an account in Nous — `identifier`.
* Only a bare `identifier` that isn't in the graph and has no `attributes` comes back `unknown_identifier`.

For the full request/response shape, see the [REST reference](/public-api/score).