Install Jentic One Beta

Connect the datawebot. Jobs API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the datawebot. Jobs 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%2Fdatawebot.com%2Fdatawebot" | 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%2Fdatawebot.com%2Fdatawebot" | 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 datawebot. Jobs API.

Discover extraction blueprints across supported ecommerce stores and resolve a target URL to its store

Submit one public product URL as a Job against a chosen blueprint_id and extract structured records

Poll a Job by id for status, credits charged, and poll_after_seconds guidance until it finishes

Page through a finished Job's records with cursor and page_size

GET STARTED

Start building with datawebot. Jobs API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use datawebot. Jobs API for, with concrete tasks.

★ Agent-Driven Product Data Extraction

An AI agent that needs live product data calls the datawebot Jobs API through Jentic: it discovers the right blueprint for a store, submits a single public product URL as a Job with that blueprint_id, then polls the Job by id and reads the extracted records against the blueprint's record_schema. Each Job extracts one URL under a store-and-operation contract, so the agent gets predictable structured output instead of raw HTML, and each Job reports its billing_status and credits.

Resolve a product URL to its blueprint, submit a Job with that blueprint_id and url, poll the job id until status is finished, and return the extracted records

Blueprint Discovery and Store Matching

Before extracting, an application lists available blueprints or searches by store to find the contract that fits a target URL. The datawebot Jobs API returns each blueprint's input_schema, record_schema, price, and availability, so a developer knows the exact inputs, output shape, and cost before spending any credits. The search endpoint also resolves a raw target URL to its store, removing guesswork about which blueprint to use.

Search blueprints with the target set to a product URL and return the matching blueprint_id, input_schema, and price

Usage and Credit Monitoring

A team running many extractions tracks spend by reading the account wallet balance and month-to-date Job totals broken down by blueprint. The datawebot Jobs API exposes the account balance and a usage summary since the start of the current UTC month, so an agent can check remaining credits before submitting a batch of Jobs. This keeps automated extraction inside a known budget.

Read the account wallet balance and the current month usage by blueprint, then decide whether to submit more Jobs

Key Endpoints

8 endpoints — the datawebot jobs api extracts structured records from a public ecommerce url using a blueprint, a store-and-operation contract that defines the inputs and output shape for one kind of page.

METHOD

PATH

DESCRIPTION

POST

/v1/jobs

Submit one public URL as one extraction Job

GET

/v1/jobs/{id}

Get Job status, credits, and poll guidance

GET

/v1/jobs/{id}/results

Read one page of a finished Job's records

GET

/v1/job-blueprints

List extraction blueprints across supported stores

GET

/v1/job-blueprints/search

Search stores or resolve a target URL to its store

GET

/v1/jobs

List your Jobs, newest first

GET

/v1/account

Get wallet balance for the account

GET

/v1/usage

Get month-to-date Job totals by blueprint

POST

/v1/jobs

Submit one public URL as one extraction Job

GET

/v1/jobs/{id}

Get Job status, credits, and poll guidance

GET

/v1/jobs/{id}/results

Read one page of a finished Job's records

GET

/v1/job-blueprints

List extraction blueprints across supported stores

GET

/v1/job-blueprints/search

Search stores or resolve a target URL to its store

GET

/v1/jobs

List your Jobs, newest first

GET

/v1/account

Get wallet balance for the account

GET

/v1/usage

Get month-to-date Job totals by blueprint

Why Jentic?

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

Setup

Wiring the datawebot Jobs API by hand means handling its dwb_ bearer key, discovering the right blueprint, sending an Idempotency-Key on every Job submit, and polling each Job by id until it finishes. Through Jentic you install once, import it from the API Directory, store the key once, and your agent calls it.

Permission scoping

Datawebot carries the blueprint and target URL in the Job request body, so rules bound which operations your agent may call, not which store or URL it may extract. You choose the operations it may call, so you can allow blueprint discovery and reading Job results while leaving Job submission, which spends credits, out unless you add it.

Credential isolation

Your datawebot 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 'extract product data from a URL' or 'list extraction blueprints', and Jentic returns the matching datawebot operation with its input schema so the agent sends the right blueprint_id and url without reading the reference docs.

FAQs

Specific to using datawebot. Jobs API through Jentic.

What authentication does the datawebot Jobs API use?

The datawebot Jobs API authenticates with a bearer API key, a dwb_ token sent in the Authorization header on every request, per its OpenAPI spec. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at call time, so it never appears in a URL or in the agent's logs.

Can I extract data from any ecommerce URL with the datawebot Jobs API?

You extract a public URL by first discovering a blueprint that covers its store and operation, then submitting the URL as a Job with that blueprint_id. Coverage is defined by the available blueprints, each of which lists its input_schema, record_schema, price, and availability, so extraction works for the stores those blueprints support rather than for arbitrary sites.

What are the rate limits for the datawebot Jobs API?

The OpenAPI spec does not specify request rate limits; Jobs are metered in credits per page fetched, and a submitted Job returns a poll_after_seconds value telling you when to check it next. For current limits and credit pricing, see the datawebot documentation at https://www.datawebot.com.

How do I run an extraction Job through Jentic?

Search Jentic with a query like 'extract product data from a URL', add the datawebot Jobs API from the directory, and store your dwb_ key once. Your agent discovers a blueprint, submits the URL to the Job submit operation with the matching blueprint_id, and polls the Job by id until its records are ready. 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 datawebot Jobs API?

Yes. Write a rule that allows only the read operations, such as listing blueprints and reading Job results, so the agent can discover contracts and fetch finished records but cannot submit new Jobs that spend credits, and every call it makes is logged. Because the blueprint and URL travel in the request body, scoping controls which operations run, not which store the agent may target.

Is there a datawebot MCP server?

Yes, datawebot offers its operations as MCP tools. You can also connect the datawebot Jobs API directly through Jentic: your agent gets the full 8-endpoint surface without loading another server's tool definitions into its context, your dwb_ key is injected at call time by your self-hosted instance instead of living in a config file, and your rules decide which operations the agent may call.

datawebot. Jobs API

AI ReadinessAI-Aware (B)66/100

See full scorecard

Official vendor OpenAPI document · agent-readyData EnrichmentData Transformationbearer8 EndpointsREST

For Agents

Discover an extraction blueprint for an ecommerce store, submit a public product URL as a Job, and read back structured records with their job status and credit cost. Turns a live product page into predictable JSON instead of raw HTML.

Use for: Find the extraction blueprint for a product page on a given store, Submit a product URL for extraction and get the job id back, Get the status and credits for a job I submitted, Retrieve the extracted records for a finished job

Not supported: Does not run a storefront, manage orders, or place purchases. Use for extracting structured data from public ecommerce URLs only.

The datawebot Jobs API extracts structured records from a public ecommerce URL using a blueprint, a store-and-operation contract that defines the inputs and output shape for one kind of page. You discover a blueprint, submit a single URL as a Job, then poll that Job for its status, credits, and extracted records instead of parsing raw HTML yourself. Each blueprint publishes its input_schema, record_schema, price, and availability, so you know the exact inputs, output fields, and cost before you spend any credits.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: ASignals: 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).

35%

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.

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: D+Signals: 4

43%

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: B+Signals: 1

67%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the datawebot. Jobs API to your agent

Capabilities

Start building with datawebot. Jobs API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

datawebot. Jobs API

Jentic AI Readiness Score

datawebot. Jobs 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