Install Jentic One Beta

Connect the Autom API Reference to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Autom API Reference, 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%2Fautom.dev%2Fautom" | 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%2Fautom.dev%2Fautom" | 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 Autom API Reference API.

Run a Google web search with location, country (gl), language (hl), and time-range (tbs) targeting and get organic results as JSON

Query Google AI Mode for a question and read its text_blocks, references, and shopping_results

Search Google News, Shopping, Images, and Videos for a query and page through the results

Find places on Google Maps with address, website, and phone, focused around an ll coordinate hint

GET STARTED

Start building with Autom API Reference API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Autom API Reference API for, with concrete tasks.

★ Web search grounding for an agent through Jentic

An AI agent answering a factual question needs fresh search results instead of its training data. Through Jentic the agent searches for 'search Google for a query', receives the Autom Google Search operation with its input schema, and calls GET /v1/google/search with the query, location, and hl language. It reads the organic results with titles, links, and snippets and cites them in its answer.

Call GET /v1/google/search with query 'latest EU AI Act guidance', gl 'gb', and hl 'en', then summarise the top five organic results with their links

Local rank and visibility checks

An SEO consultant checks where a client appears in results for a city. GET /v1/google/search accepts a human-readable location such as 'Paris, France' or a uule code, plus google_domain and gl, so the same keyword can be checked from different markets. GET /v1/finder/google-locations returns the location strings and IDs Google supports, and GET /v1/google/maps returns local place listings with address and phone.

Call GET /v1/finder/google-locations with query 'Lyon', then GET /v1/google/search with query 'plombier lyon', location from the finder result, and google_domain 'google.fr', and report the position of a client domain

Job and shopping market scans

A researcher tracks hiring or product pricing in a market. GET /v1/google/jobs returns structured job listings with title, company, and location and pages with next_page_token, while GET /v1/google/shopping returns product listings for a query with location and country targeting. Both return JSON that can be stored and compared over time.

Call GET /v1/google/jobs with query 'data engineer' and location 'Berlin, Germany', follow next_page_token for two more pages, and count listings by company

Multi-engine fallback search

A pipeline that must always return results cannot depend on one engine. GET /v1/search walks engines in order, by default Google, Brave, Bing, DuckDuckGo, Yahoo, and Baidu, and returns the first organic results found. The engines parameter sets a custom order, and dedicated endpoints such as GET /v1/brave/search and GET /v1/bing/search query one engine directly.

Call GET /v1/search with query 'open source vector database' and engines 'brave,bing,duckduckgo', then list the returned titles and links

Key Endpoints

40 endpoints — the autom api returns structured search engine results from google, bing, brave, duckduckgo, and yahoo as json.

METHOD

PATH

DESCRIPTION

GET

/v1/google/search

Google Search results

GET

/v1/google/search/ai

Google AI Mode answer

GET

/v1/google/news

Google News results

GET

/v1/google/maps

Google Maps place results

GET

/v1/google/jobs

Google Jobs listings

GET

/v1/search

Multi-engine fallback search

GET

/v1/bing/search

Bing Search results

GET

/v1/jobs/{id}

Poll an asynchronous job

GET

/v1/google/search

Google Search results

GET

/v1/google/search/ai

Google AI Mode answer

GET

/v1/google/news

Google News results

GET

/v1/google/maps

Google Maps place results

GET

/v1/google/jobs

Google Jobs listings

GET

/v1/search

Multi-engine fallback search

GET

/v1/bing/search

Bing Search results

GET

/v1/jobs/{id}

Poll an asynchronous job

Why Jentic?

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

Setup

Wiring Autom by hand means sending the x-api-key header, learning separate parameter sets for each engine, and handling Google location targeting with location or uule. Through Jentic you install once, import Autom from the API Directory, store the key once, and your agent calls it.

Permission scoping

Autom puts the engine and result type in the URL path (/v1/google/news, /v1/bing/search), so a rule can limit your agent to specific engines and result types. You choose the operations it may call, so ones that cost more credits, like Google AI Mode, are not included unless you add them.

Credential isolation

Your Autom API 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 'search Google for a query' or 'find places on Google Maps', and Jentic returns the matching Autom operation with its input schema so the agent calls the right engine endpoint without browsing the reference docs.

FAQs

Specific to using Autom API Reference API through Jentic.

What authentication does the Autom API use?

The Autom API authenticates with an API key in the `x-api-key` request header, declared as the ApiKeyAuth scheme in its OpenAPI spec. Through Jentic the key is held by your own Jentic One instance and attached to each call, so the agent never sees it.

Which search engines does the Autom API cover?

The spec has endpoints for Google (web search, Search Light, AI Mode, News, Images, Videos, Shopping, Maps, Jobs, and autocomplete), Bing, Brave (web, images, and videos), DuckDuckGo, and Yahoo. GET /v1/search also walks engines in order and can include Baidu in its default fallback list.

How are Autom API calls priced?

Autom bills in credits. Its spec lists most search endpoints at one credit and Google AI Mode at three credits, while finder endpoints and job polling are free. GET /v1/usage returns your current usage and credit balance.

What are the rate limits for the Autom API?

The OpenAPI spec does not specify rate limits. Check the Autom documentation at https://autom.dev for current limits on your plan.

How do I search Google News through Jentic?

Search Jentic for 'get Google News results for a topic', load the GET /v1/google/news operation, and execute it with query plus optional gl, hl, and page. The agent receives structured news results it can summarise. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Is there an Autom MCP server?

You don't need an MCP server to give your agent Autom. Jentic connects it directly from the API Directory: import it, store your API key once, and your agent discovers the search operations it needs on demand, so a long list of per-engine tool definitions never has to sit in its context.

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

Yes. If the agent only needs web and news results, write a rule that allows GET /v1/google/search and GET /v1/google/news and nothing else. It can then run those two searches but not the AI Mode endpoint that costs more credits, and every call it makes is logged.

Autom API Reference

AI ReadinessFoundational (C+)57/100

See full scorecard

Official vendor OpenAPI document · agent-readyMarketingSeo SemapiKey40 EndpointsREST

For Agents

Fetch structured Google, Bing, Brave, DuckDuckGo, and Yahoo search results, including Google News, Shopping, Maps, Jobs, Images, and AI Mode answers, with location and language targeting.

Use for: Search Google for 'best espresso machine 2026' from New York, Get the latest Google News results about a company, Find coffee shops near a coordinate on Google Maps, List all Google Jobs postings for 'data engineer' in Berlin

Not supported: Does not crawl full web pages, index your own content, or run paid ad campaigns. Use for fetching structured search engine results only.

The Autom API returns structured search engine results from Google, Bing, Brave, DuckDuckGo, and Yahoo as JSON. Google coverage spans web search, Search Light, AI Mode, News, Images, Videos, Shopping, Maps, Jobs, and autocomplete, with location, country, language, and Google-domain targeting. A single /v1/search call can walk several engines in order and return the first organic results found. Finder endpoints look up supported Google locations, languages, and countries, and long-running requests such as image searches can run as jobs that are polled for their result.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: BSignals: 4

81%

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).

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: B+Signals: 4

14%

Example Density

How richly the API is illustrated with examples.

84%

Example Validity

Percentage of examples that conform to their schemas.

75%

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

41%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

95%

OperationId Quality

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

16%

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: BSignals: 1

64%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Autom API Reference to your agent

Capabilities

Start building with Autom API Reference API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Autom API Reference

Jentic AI Readiness Score

Autom API Reference

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