canonical: https://jentic.com/apis/citehawk.com/citehawk

# CiteHawk API

The CiteHawk API serves one workspace's AI-visibility (GEO) data, scoped to the API key in the Authorization header so callers never pass a workspace id or read across tenants. It lists the brands the workspace tracks, returns a daily visibility metrics timeseries with the latest KPIs, and reports the workspace's Visibility and Brand Health scores with their history. It also returns the recent AI collection responses, with their citations, that power those metrics, starts white-label prospect scans and reports their status, and exposes a Looker Studio schema and daily metric rows for connectors.

## For AI agents

Pull a workspace's AI-visibility data: tracked brands, a daily metrics timeseries with KPIs, Visibility and Brand Health scores, and the cited AI responses behind them. Start white-label prospect scans, all scoped to the API key's workspace.

## Scope

Does not run paid search campaigns, publish content, or do classic keyword-rank SEO. Use for AI-visibility (GEO) metrics, scores, and prospect scans only.

## Capabilities

- List the brands a workspace tracks for AI visibility
- Pull a daily visibility metrics timeseries with the latest KPIs
- Report Visibility and Brand Health scores with their history
- Return the recent AI responses, with citations, behind the metrics
- Start a white-label prospect scan and check its status
- Expose a Looker Studio schema and daily metric rows for connectors

## Use cases

### Agent-Monitored AI Visibility

An AI agent pulls a workspace's visibility metrics, scores, and the cited AI responses behind them through Jentic to monitor how brands show up in AI answers. The API key in the Authorization header scopes every read to one workspace, so the agent cannot read across tenants, and a rule can keep it on the read operations. The agent finds each read by intent and calls it directly.

Example prompt: Pull the latest Visibility score and the AI responses behind it, then flag any drop week over week.

### GEO Reporting and Dashboards

A reporting tool pulls the daily metrics timeseries and the latest KPIs for a workspace and feeds a Looker Studio connector using the exposed schema and daily metric rows. CiteHawk returns Visibility and Brand Health scores with history, so the dashboard shows trend, not just a snapshot. Teams use this to report GEO performance to stakeholders.

Example prompt: Fetch the daily metric rows for a date range and build a Visibility trend for the workspace.

### White-Label Prospect Scans

An agency starts a white-label prospect scan to show a potential client their AI visibility, then polls the scan by id for status and results. CiteHawk runs the scan under the workspace key and returns the scored output when it finishes. Agencies use this as a lead-generation step before onboarding.

Example prompt: Start a prospect scan for a domain and poll it by id until the results are ready.

## Key endpoints

| Method | Path | Description |
| --- | --- | --- |
| GET | `/brands` | List tracked brands |
| GET | `/metrics` | Daily metrics timeseries and latest KPIs |
| GET | `/responses` | Recent AI collection responses |
| GET | `/scores` | Latest Visibility and Brand Health scores |
| POST | `/prospect-scans` | Start a white-label prospect scan |
| GET | `/prospect-scans/{id}` | Prospect scan status and results |

## Key resources

- **Brands** — The brands a workspace tracks for AI visibility
- **Metrics and scores** — Daily visibility metrics timeseries, latest KPIs, and Visibility and Brand Health scores with history
- **Responses** — Recent AI collection responses with the citations behind the metrics
- **Prospect scans** — Start a white-label prospect scan and read its status and results
- **Looker Studio connector** — Field schema and daily metric rows for the Looker Studio connector

## AI readiness

This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.

- **Score:** 67 / 100
- **Maturity:** AI-Aware
- **Dimensions:**
  - Foundational Compliance: 100 / 100
  - Developer Experience & Jentic Compatibility: 65 / 100
  - AI-Readiness & Agent Experience: 46 / 100
  - Agent Usability: 94 / 100
  - Security: 60 / 100
  - AI Discoverability: 78 / 100
- **View full report:** https://jentic.com/apis/citehawk.com/citehawk/scorecard
- **How the score is calculated:** https://docs.jentic.com/reference/api-readiness-framework/overview/
- **More about the dimensions:** https://docs.jentic.com/reference/api-readiness-framework/specification/#dimensional-model-overview

### Score it yourself

Every API in the directory is allowlisted, so you can re-score it with no key required.

- **Score your own API:** https://jentic.com/scorecard.md
- **Scoring CLI agent skill:** https://github.com/jentic/jentic-api-scorecard/blob/main/skills/jentic-api-scorecard/SKILL.md

```sh
npx @jentic/api-scorecard-cli score <openapi-url>
```

## Why Jentic

- **Setup:** Wiring the CiteHawk API by hand means sending a workspace-scoped key as a bearer token and knowing that every read is pinned to that one workspace with no workspace id to pass. Through Jentic you install once, import CiteHawk from the API Directory, store the key once, and your agent calls it.
- **Permission scoping:** CiteHawk scopes the key to one workspace, and a rule can further bound your agent to the read operations such as metrics, scores, and brands. You choose the operations it may call, so starting white-label prospect scans is not included unless you add it.
- **Credential handling:** Your CiteHawk key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
- **Discovery method:** Agents search Jentic by intent such as 'get the visibility score for my brand', and Jentic returns the matching CiteHawk operation with its input schema so the agent calls the right endpoint without reading the reference docs.

## Related APIs

- **Semrush** — A broad SEO and SEM suite for keywords, ranks, and site audits.
- **Ahrefs** — SEO toolset for backlinks, keyword ranks, and site audits.
- **Plausible** — Privacy-friendly web analytics for site traffic and conversions.

## FAQ

### What authentication does the CiteHawk API use?

The CiteHawk API authenticates with a CiteHawk API key sent as a bearer token in the Authorization header, per its OpenAPI spec. The key scopes every request to the single workspace that owns it, so callers never pass a workspace id and cannot read across tenants. Through Jentic the key is stored encrypted by your own Jentic One instance and injected only at call time.

### Can I pull AI-visibility metrics and scores with the CiteHawk API?

Yes. The API lists the brands a workspace tracks, returns a daily metrics timeseries with the latest KPIs, and reports Visibility and Brand Health scores with history. It also returns the AI responses, with citations, that power those metrics.

### What are the rate limits for the CiteHawk API?

The OpenAPI spec does not specify rate limits for the CiteHawk API. Check the vendor's documentation at https://www.citehawk.com for current limits before pointing an agent at it.

### Can I limit what my agent is allowed to do with the CiteHawk API?

Yes. Write a rule that allows only the read operations, such as metrics, scores, and brands, so the agent reports on visibility but cannot start white-label prospect scans. Because the key is already scoped to one workspace, the agent cannot reach another tenant's data, and every call is logged.

### Is there a CiteHawk MCP server?

You don't need an MCP server to give your agent the CiteHawk API. Jentic connects it directly from the API Directory: import it, store your workspace key once, and your agent calls the brands, metrics, and scores operations. You skip loading another server's tool definitions into the agent's context.

### How do I pull AI-visibility data through Jentic?

Add the CiteHawk API from the Jentic API Directory and store your workspace key once. Your agent then searches by intent such as 'pull my brand's AI visibility metrics' and Jentic returns the matching operation with its input schema, so it can read the scores and the AI responses behind them. To run it on your own infrastructure, install Jentic One from its GitHub repo.