Install Jentic One Beta

Connect the LLMSE API to your agent

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

Run a full website audit with auditUrl that combines classification, SEO, E-E-A-T, AEO, readability, WCAG, GARM, AI disclosure, and privacy checks in one call

Score a page's answer-engine readiness with analyzeAeo, including checks against robots.txt and llms.txt

Check a page against WCAG accessibility rules with analyzeWcag and return categorized issues

Rate brand safety under the GARM framework with analyzeGarm for a URL or a given category and sentiment

GET STARTED

Start building with LLMSE API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use LLMSE API for, with concrete tasks.

★ Agent-run site audits through Jentic

A coding or marketing agent asks Jentic for the LLMSE operation that audits a website, then calls auditUrl with the page URL. The response bundles classification, SEO, E-E-A-T, AEO, readability, WCAG, GARM, AI disclosure, privacy, advertiser matches, and similar sites, so the agent can turn it into a prioritized fix list. Sub-analyses that fail come back as an error object rather than failing the whole audit.

Search Jentic for 'audit a website for SEO and accessibility', call auditUrl with url 'https://example.com', and list the WCAG and SEO issues it returns

Answer-engine readiness checks

Before publishing, a team calls analyzeAeo on each key page to score how well it is structured for AI answer engines. The audit variant also fetches robots.txt, llms.txt, and llms-full.txt, so the result shows whether agents and AI crawlers can read the site at all.

Call analyzeAeo with url 'https://example.com/pricing' and report the lowest-scoring metrics

Brand safety review for ad placements

An ad operations workflow runs analyzeGarm on a publisher URL to score brand safety, then calls selectAdvertiser to rank advertising networks against the page's classification, with optional age, gender, sentiment, and min_cpm filters. Matches are scored on category, age, gender, and sentiment fit, with CPM as tiebreaker.

Call analyzeGarm with url 'https://news.example.com/article', then call selectAdvertiser with the same url and limit 5

Competitive site discovery

A researcher calls classifyUrl to see how LLMSE categorizes a competitor's site, then findSimilarSites to list other websites in the same category and subcategory, using cached classifications when available. The list seeds a competitive analysis or outreach shortlist.

Call findSimilarSites with url 'https://competitor.example.com' and limit 10, then group the results by subcategory

Key Endpoints

15 endpoints — the llmse api analyzes a website from a single url, returning a content classification plus separate scores for seo, e-e-a-t, aeo (answer engine optimization), readability, wcag accessibility, garm brand safety, ai disclosure, and privacy compliance.

METHOD

PATH

DESCRIPTION

GET

/api/v1/audit

Comprehensive audit

GET

/api/v1/classify

Classify a URL

GET

/api/v1/seo

Analyze SEO

GET

/api/v1/aeo

Analyze AEO

GET

/api/v1/wcag

Analyze WCAG accessibility

GET

/api/v1/garm

Analyze GARM brand safety

GET

/api/v1/advertisers

Match advertisers

GET

/api/v1/similar

Find similar sites

GET

/api/v1/audit

Comprehensive audit

GET

/api/v1/classify

Classify a URL

GET

/api/v1/seo

Analyze SEO

GET

/api/v1/aeo

Analyze AEO

GET

/api/v1/wcag

Analyze WCAG accessibility

GET

/api/v1/garm

Analyze GARM brand safety

GET

/api/v1/advertisers

Match advertisers

GET

/api/v1/similar

Find similar sites

Why Jentic?

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

Setup

Wiring LLMSE by hand means handling its optional bearer token, tracking the X-RateLimit headers, and honoring Retry-After on 429 responses yourself. Through Jentic you install once, import LLMSE from the API Directory, and your agent calls it.

Permission scoping

LLMSE takes the target page as a url query parameter, so rules bound which operations your agent may call rather than which sites it analyzes. You choose the operations it may call, so the user-agent submission write is not included unless you add it.

Credential isolation

LLMSE endpoints work without a key. If you add a bearer token, it is held encrypted by your own Jentic One instance and injected at execution time, and it never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'audit a website' or 'check brand safety of a URL', and Jentic returns the matching LLMSE operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using LLMSE API through Jentic.

What authentication does the LLMSE API use?

Per its OpenAPI spec, every LLMSE endpoint works without authentication, and an optional HTTP bearer scheme accepts a Descope JWT. Authenticated callers currently receive the same rate limits as anonymous ones. If you do add a token through Jentic, it is stored encrypted by your own Jentic One instance and injected at call time.

Can I check WCAG accessibility with the LLMSE API?

Yes. Call GET /api/v1/wcag with the url query parameter and LLMSE returns a WCAG accessibility result with categorized issues for that page. GET /api/v1/audit includes the same WCAG check alongside SEO, E-E-A-T, AEO, and the other analyses.

What are the rate limits for the LLMSE API?

The OpenAPI spec states per-domain rate limiting of 1 request per domain per 60 seconds, and cached results return without consuming the limit. Responses carry X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset headers, and a 429 response includes Retry-After.

How do I run a full website audit with the LLMSE API through Jentic?

Install Jentic One with `curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh`, add the LLMSE API from the Jentic API Directory, and have your agent search for 'audit a website'. Jentic returns auditUrl (GET /api/v1/audit) with its schema, and the agent passes the page url. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Is there an LLMSE MCP server?

You don't need an MCP server to give your agent the LLMSE API. Jentic connects it directly from the API Directory: import it and your agent calls it, with operations discovered on demand instead of loading extra tool definitions into its context.

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

Yes. For an audit-only agent, write a rule that allows auditUrl, analyzeSeo, analyzeAeo, and analyzeWcag. The agent can analyze pages but cannot call submitUserAgent, the one write operation, because it is not in the allowed set, and every call it makes is logged.

What does the LLMSE comprehensive audit include?

GET /api/v1/audit combines classification, SEO, E-E-A-T, AEO with citation and agent readiness, readability, WCAG, GARM, AI disclosure, privacy compliance, advertiser matching, and similar site discovery in one request. It fetches the page once, also reads robots.txt, llms.txt, and llms-full.txt, and returns an error object for any sub-analysis that fails.

LLMSE API

AI ReadinessAI-Aware (B-)61/100

See full scorecard

Official vendor OpenAPI document · agent-readyMarketingSeo Semhttp bearer (optional)15 EndpointsREST

For Agents

Audit any website URL for SEO, E-E-A-T, answer-engine readiness, readability, WCAG accessibility, brand safety, and privacy compliance, classify its content, find similar sites, and match advertisers to it.

Use for: Audit this landing page for SEO and accessibility problems, Check whether my site is ready for AI answer engines, Classify what category this website belongs to, Find sites similar to a competitor's homepage

Not supported: Does not track keyword rankings, crawl entire sites, or monitor brand mentions in AI answers over time. Use for single-URL website analysis, classification, and advertiser matching only.

The LLMSE API analyzes a website from a single URL, returning a content classification plus separate scores for SEO, E-E-A-T, AEO (Answer Engine Optimization), readability, WCAG accessibility, GARM brand safety, AI disclosure, and privacy compliance. A comprehensive audit runs all of these in one request, fetching the page once and also checking robots.txt, llms.txt, and llms-full.txt for agent readiness. It also matches advertising networks to a page's classification demographics and returns similar sites from the same category and subcategory, and every endpoint works without authentication under per-domain rate limiting.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: D+Signals: 4

94%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

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: BSignals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

48%

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

41%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

100%

OperationId Quality

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

50%

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: B-Signals: 1

60%

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: ASignals: 1

84%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the LLMSE API to your agent

Capabilities

Start building with LLMSE API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

LLMSE API

Jentic AI Readiness Score

LLMSE 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