Install Jentic One Beta

Connect the LLM Pulse API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the LLM Pulse 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%2Fllmpulse.ai%2Fllmpulse" | 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%2Fllmpulse.ai%2Fllmpulse" | 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 LLM Pulse API.

Read Share of Voice over time, current, or by breakdown for a project with getShareOfVoice and the view parameter

List brand and competitor mentions and citations found in AI answers from ChatGPT, Perplexity, Gemini, and Google AI Overviews

Bulk-create tracked prompts for a project with country_code and language_code, and group them into tags

Add competitors by brand_name and domain so their mentions and citations are tracked alongside yours

GET STARTED

Start building with LLM Pulse API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use LLM Pulse API for, with concrete tasks.

★ Agent-run AI visibility reporting through Jentic

A marketing agent asks Jentic for the LLM Pulse operations that report brand visibility, then calls getSummary and getShareOfVoice for a project_id over a range of days and pulls the top cited sources with getTopSources. It writes a weekly digest of how often the brand appears in AI answers versus competitors. The bearer key stays in your self-hosted Jentic One instance.

Search Jentic for 'get share of voice in AI answers', call getShareOfVoice with project_id 123, range 30, and view 'over_time', then summarize the trend

Competitor tracking in AI answers

Add each rival with createCompetitor using project_id, brand_name, and domain, then read listCompetitorMentions and listCompetitorCitations to see where AI answers recommend them instead of you. listAllMentions combines brand and competitor mentions into one feed for side-by-side review.

Call createCompetitor with project_id 123, brand_name 'Acme', and domain 'acme.com', then call listCompetitorMentions for project_id 123

GEO audits for key pages

Create GEO audits with createGeoAudits by passing project_id, a target, and audit_types, optionally on a cadence. Trigger a run with runGeoAudit, read its findings with listGeoAuditFindings, and use compareGeoAuditRuns with from_run and to_run to confirm that fixes landed between releases.

Call runGeoAudit for geo_audit_id 42, then call listGeoAuditFindings for the new run sequence and list the open findings

Real-time alerts on mentions and sentiment

Register a public HTTPS target_url with createWebhook for event types such as mention.created, citation.created, or sentiment.negative_detected. LLM Pulse sends signed event payloads, and sampleWebhookPayloads returns example bodies so the receiver can be built before the first live event arrives.

Call createWebhook with project_id 123, event_type 'sentiment.negative_detected', and target_url 'https://hooks.example.org/llmpulse'

Key Endpoints

127 endpoints — the llm pulse api measures how a brand appears in ai-generated answers from chatgpt, perplexity, gemini, google ai overviews, and google ai mode, returning brand mentions, citations, sentiment, and share of voice for each tracked project.

METHOD

PATH

DESCRIPTION

GET

/metrics/sov

Share of Voice

GET

/metrics/summary

Aggregated metrics summary

GET

/dimensions/mentions

List brand mentions

GET

/dimensions/citations

List brand citations

POST

/prompts

Bulk-create tracked prompts

POST

/competitors

Add a competitor

POST

/geo_audits

Create GEO audits

POST

/webhooks

Create a webhook subscription

GET

/metrics/sov

Share of Voice

GET

/metrics/summary

Aggregated metrics summary

GET

/dimensions/mentions

List brand mentions

GET

/dimensions/citations

List brand citations

POST

/prompts

Bulk-create tracked prompts

POST

/competitors

Add a competitor

POST

/geo_audits

Create GEO audits

POST

/webhooks

Create a webhook subscription

Why Jentic?

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

Setup

Wiring LLM Pulse by hand means handling its bearer API key, passing project_id on most operations, and paging through mention and citation lists yourself. Through Jentic you install once, import LLM Pulse from the API Directory, store the key once, and your agent calls it.

Permission scoping

LLM Pulse passes project_id as a query or body field on most operations, so rules bound which operations your agent may call rather than which project. You choose the operations it may call, so destructive ones like deleteCompetitor, removeTeamMember, or deleteGeoAudit are not included unless you add them.

Credential isolation

Your LLM Pulse 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.

Intent-based discovery

Agents search Jentic by intent such as 'share of voice in AI answers' or 'run a GEO audit', and Jentic returns the matching LLM Pulse operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using LLM Pulse API through Jentic.

What authentication does the LLM Pulse API use?

Per its OpenAPI spec, REST calls use an HTTP bearer API key managed in your LLM Pulse account (Scale plan or above), and a key can be limited to one or several projects when it is created. The spec also declares an OAuth2 scheme (OAuth 2.1 with PKCE) used by the MCP endpoint alongside the bearer key. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at call time.

Can I read Share of Voice in AI answers with the LLM Pulse API?

Yes. Call GET /metrics/sov with a project_id and a range or from and to dates, and set view to over_time, current, or breakdown. You can narrow it by competitors and model, and GET /metrics/timeseries and GET /metrics/summary return the broader visibility metrics for the same project.

What are the rate limits for the LLM Pulse API?

The OpenAPI spec does not publish fixed numbers. Call GET /account to read your account plan, quota usage, and rate limits as they apply to your key, and see the LLM Pulse API reference for plan details.

How do I add tracked prompts with the LLM Pulse API through Jentic?

Install Jentic One with `curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh`, add the LLM Pulse API from the Jentic API Directory, and have your agent search for 'bulk create tracked prompts'. Jentic returns createPrompts (POST /prompts) with its schema, and the agent sends project_id, prompts, country_code, and language_code. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Is there an LLM Pulse MCP server?

Yes - LLM Pulse offers an MCP endpoint at /mcp. You can also connect the LLM Pulse API directly through Jentic: your agent gets the full 127-endpoint surface from the spec without loading another server's tool definitions into its context, your credential is injected at call time by your self-hosted instance instead of living in an MCP config, and your rules decide which operations the agent may call.

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

Yes. For a reporting agent, write a rule that allows getSummary, getShareOfVoice, getTopSources, listMentions, and listCitations. The agent can then read visibility data and compile reports, but it cannot delete competitors, remove team members, or create webhooks, because those operations are not in the allowed set, and every call it makes is logged.

Can the LLM Pulse API send events to my own endpoint?

Yes. POST /webhooks takes project_id, event_type, and a public HTTPS target_url that receives signed payloads. Event types include mention.created, citation.created, prompt_execution.completed, sentiment.negative_detected, and geo_audit_run.completed, and GET /webhooks/sample/{event_type} returns sample payloads for testing.

LLM Pulse API

AI ReadinessAI-Aware (B)64/100

See full scorecard

Official vendor OpenAPI document · agent-readyMarketingSeo Semhttp bearer, oauth2127 EndpointsREST

For Agents

Track brand visibility in AI answers: manage projects, prompts, and competitors in LLM Pulse, read Share of Voice, mentions, citations, and sentiment, and run GEO audits and recommendation reports.

Use for: What is our Share of Voice in ChatGPT answers this month?, List all new brand mentions in AI answers since last week, Add a competitor to my LLM Pulse project, I want to track a new set of buyer prompts in Germany

Not supported: Does not run paid ad campaigns, host websites, or call language models on your behalf for chat. Use for tracking and improving brand visibility in AI-generated answers only.

The LLM Pulse API measures how a brand appears in AI-generated answers from ChatGPT, Perplexity, Gemini, Google AI Overviews, and Google AI Mode, returning brand mentions, citations, sentiment, and Share of Voice for each tracked project. It lets you set up projects, competitors, and tracked prompts, then read time-series and summary metrics, the individual AI responses behind them, and the sources those answers cite. Beyond tracking, it runs GEO audits and technical GEO reports, launches recommendation runs and GEO Writer content tasks, reads Google Search Console and AI referral traffic for the project, and sends signed webhook events such as mention.created or geo_audit_run.completed.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A-Signals: 4

95%

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).

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: B-Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

45%

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: D+Signals: 4

42%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

99%

OperationId Quality

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

54%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A-Signals: 1

76%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: A-Signals: 1

75%

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: A-Signals: 1

72%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the LLM Pulse API to your agent

Capabilities

Start building with LLM Pulse API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

LLM Pulse API

Jentic AI Readiness Score

LLM Pulse 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