AIMentionTracker API
49
AI ReadinessFoundational (D+)49/100
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.
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.
1
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" | sh2
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 instanceJentic 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
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
Jentic AI Readiness Score
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.
A
AIMentionTracker API
- Foundational (D+)49/100
93
Foundational Compliance63
Developer Experience & Jentic Compatibility26
AI-Readiness & Agent Experience94
Agent Usability50
Security50
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.2
Show dimension breakdown
93
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.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: BSignals: 4
2%
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.
26
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.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
0%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
46%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
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.
50
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).
50
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: C-Signals: 1
50%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>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.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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.
GET STARTED