Install Jentic One Beta

Connect the AIMentionTracker API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the AIMentionTracker API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Faimentiontracker.ai%2Faimentiontracker" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Faimentiontracker.ai%2Faimentiontracker" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with AIMentionTracker API.

Read a brand's presence per AI engine over a trailing window, reported separately per engine

Retrieve the stored AI answers with their brand mentions and citations

List the source domains the engines read before answering

Review findings about a brand from the alerts feed, newest first

List the brands a key may read and the rules for reading the numbers

GET STARTED

Start building with AIMentionTracker API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use AIMentionTracker API for, with concrete tasks.

★ AI visibility reporting agent

An AI agent building a weekly brand report needs presence figures that respect how the data must be read. Through Jentic the agent searches for 'read brand visibility per engine', loads the AIMentionTracker operation with its input schema, and reads visibility with the key injected at call time. Because each engine is reported separately rather than averaged, the agent presents per-engine figures instead of a single blended number.

Read GET /visibility for a brand over a trailing window and report presence per engine without averaging across engines

Citation and source analysis

A content team wants to know which sites AI assistants read instead of their own. AIMentionTracker returns the stored answers with their citations and a sources view of the domains engines consult before answering. An agent can pull the sources, rank them by frequency, and flag competitor domains that are being cited.

Read GET /sources for a brand and GET /answers to list the domains cited most often in AI answers

Visibility alerting

A marketer wants to hear only about real moves in brand visibility, not noise. AIMentionTracker suppresses findings that sit inside their own margin of error and surfaces the rest through an alerts feed, newest first. An agent can poll the alerts, read the thresholds from the account endpoint, and summarise only the findings that cleared the margin.

Poll GET /alerts for a brand and summarise each finding, using GET /me to report the thresholds applied

Key Endpoints

6 endpoints — the aimentiontracker api reports how often ai assistants name and cite a brand and which sites they read instead.

METHOD

PATH

DESCRIPTION

GET

/visibility

Presence per engine and brand over a trailing window

GET

/answers

Stored answers with mentions and citations

GET

/sources

Domains the engines read before answering

GET

/alerts

Findings about a brand, newest first

GET

/brands

Brands this key may read

GET

/me

Key identity, thresholds, and reading rules

GET

/visibility

Presence per engine and brand over a trailing window

GET

/answers

Stored answers with mentions and citations

GET

/sources

Domains the engines read before answering

GET

/alerts

Findings about a brand, newest first

GET

/brands

Brands this key may read

GET

/me

Key identity, thresholds, and reading rules

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring AIMentionTracker by hand means obtaining an organisation key, sending it as an X-API-Key or bearer header, and carrying the reading rules so per-engine numbers are not averaged. Through Jentic you install once, import AIMentionTracker from the API Directory, store the key once, and your agent calls it.

Permission scoping

The API is read-only with brands scoped to the key, and each view sits at its own path such as /visibility and /alerts, so a rule can pin your agent to the reads it needs. You choose which operations it may call, so the answers or sources endpoints are not included unless you add them.

Credential isolation

Your AIMentionTracker key is stored once, encrypted, by your own Jentic One instance and attached as the X-API-Key or bearer header at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'read brand visibility per engine' or 'check brand alerts', and Jentic returns the matching AIMentionTracker operation with its input schema so the agent calls the right endpoint over the trailing window it needs.

FAQs

Specific to using AIMentionTracker API through Jentic.

What authentication does the AIMentionTracker API use?

It authenticates with an API key sent in the X-API-Key request header, and the spec notes an Authorization bearer header is accepted equivalently. Keys belong to the organisation rather than a person, so they survive staff changes. Through Jentic the key is held by your own instance and attached per call.

How should an agent read the visibility numbers from the AIMentionTracker API?

Each engine is reported separately and should not be averaged, since engines disagree and a blend describes no real surface. A null rate means there were not enough answers yet rather than zero, and an empty alerts list does not mean nothing changed, because moves inside the margin of error are suppressed. GET /me returns the live thresholds behind these rules.

What are the rate limits for the AIMentionTracker API?

The OpenAPI spec does not declare numeric rate limits. Check the AIMentionTracker documentation for the current per-plan allowance, and note that most endpoints return a trailing window, so frequent polling rarely adds new data.

Is there an AIMentionTracker MCP server?

You do not need an MCP server to give your agent AIMentionTracker. Jentic connects it directly from the API Directory: import it, store your key once, and your agent calls the visibility, answers, and alerts operations it needs.

How do I read brand visibility through Jentic?

Search Jentic for 'read brand visibility per engine', load the GET /visibility operation, and run it for a brand over a trailing window. The key is attached by your instance, and the response keeps each engine separate. To run it on your own infrastructure, install Jentic One from its GitHub repo.

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

Yes. The API is read-only and a key may only read its own organisation's brands, so a rule can allow the agent just the reads a task needs, such as visibility and alerts, while leaving the answers or sources endpoints out unless you add them. The agent stays bound to the operations you grant, and every call is logged.

AIMentionTracker API

AI ReadinessFoundational (D+)49/100

See full scorecard

Official vendor OpenAPI document · agent-readyMarketingSeo SemapiKey6 EndpointsREST

For Agents

Read how AI assistants mention and cite a brand: presence per engine, the answers behind the numbers, the sources engines read, and alerts, for AI agents.

Use for: Get a brand's visibility per AI engine, List the sources the engines cited for my brand, Check the latest alerts about my brand, Retrieve the stored answers that mention my brand

Not supported: Does not post content, run ad campaigns, or change what AI assistants say. Use for reading how AI assistants mention and cite your brand only.

The AIMentionTracker API reports how often AI assistants name and cite a brand and which sites they read instead. It returns presence per engine and per brand over a trailing window, with each engine reported separately rather than averaged, plus the stored answers behind those numbers with their mentions and citations. A sources view lists the domains the engines read before answering, and an alerts feed surfaces findings newest-first when a brand's visibility moves beyond its margin of error. GET /me returns the live thresholds and the rules for reading the numbers.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A+Signals: 4

98%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

100%

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

75%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: BSignals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

50%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: FSignals: 4

56%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

46%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: C-Signals: 1

50%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: C-Signals: 1

50%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the AIMentionTracker API to your agent

Capabilities

Start building with AIMentionTracker API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

AIMentionTracker API

Jentic AI Readiness Score

AIMentionTracker API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness