Install Jentic One Beta

Connect the Hunch API to your agent

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

Ask one yes/no question of up to 40 texts and get a probability for each

Sort texts into one of up to 255 labelled options, with confidence and per-option probabilities

Place each text on a 2 to 10 level scale such as angry to delighted

Answer up to 10 yes/no questions per text in a single multi call

Price a job before running it with the free quote call, given rows and questions

GET STARTED

Start building with Hunch API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Hunch API for, with concrete tasks.

★ Bulk text judgement from an agent through Jentic

A developer's agent hands Hunch the repetitive part of a review: hundreds of short texts that each need the same judgement. Through Jentic the agent finds the ask operation by intent and sends rows in batches of 40; the Hunch key is injected by the developer's own Jentic One instance, and a rule can stop the agent from creating checkout links.

Call GET /v1/quote with rows 120 to confirm the cost, then call POST /v1/ask three times with kind noul, the question 'Is this lead a decision maker who can approve a purchase without asking someone else?', and 40 rows each

Support ticket triage

Route incoming tickets by sorting them into categories. The choice kind takes a list of options, each written as a label or 'label: description', and returns the chosen label with a confidence value and a probability per option, so low-confidence tickets can go to a person.

Call POST /v1/ask with kind choice, options ['billing: invoices and charges', 'refund', 'bug', 'other'], and the 40 newest ticket texts as rows, and flag any result whose conf is below 0.6

Review sentiment scoring

Put customer reviews on a scale rather than a binary. The score kind takes 2 to 10 options ordered low to high, such as angry, disappointed, neutral, happy, and delighted, and returns a weighted position, the index and label of the most likely level, and a confidence.

Call POST /v1/ask with kind score, the question 'How does the reviewer feel about the product overall?', options angry, disappointed, neutral, happy, delighted, and 40 review texts as rows

Multi-question lead qualification

Answer several yes/no questions about each text in one pass. The multi kind takes 1 to 10 questions and returns one probability per question for every row, billed one credit per question per text, so a lead can be checked for buying power, urgency, and frustration together.

Call POST /v1/ask with kind multi, questions ['Can they buy?', 'Is it urgent?', 'Are they angry?'], and 40 inbound email snippets as rows, and list the rows where the first two probabilities are above 0.7

Key Endpoints

5 endpoints — the hunch api judges batches of short texts against a question and returns one typed answer per text instead of generated prose.

METHOD

PATH

DESCRIPTION

POST

/v1/ask

Judge up to 40 texts

GET

/v1/quote

Price a job before running it

GET

/v1/balance

Credits left on this key

POST

/trial

Get a free 100-row key

POST

/v1/checkout

Create a buy link for a person to open

POST

/v1/ask

Judge up to 40 texts

GET

/v1/quote

Price a job before running it

GET

/v1/balance

Credits left on this key

POST

/trial

Get a free 100-row key

POST

/v1/checkout

Create a buy link for a person to open

Why Jentic?

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

Setup

Wiring the Hunch API by hand means minting or buying a key, sending it as a bearer token, batching rows into groups of 40, and handling the out-of-credits case. Through Jentic you install once, import Hunch from the API Directory, store the key once, and your agent calls it.

Permission scoping

Hunch carries the texts and the question inside the request body, so rules bound which operations your agent may call, not what it asks. You choose the operations it may call, so trial key minting and checkout link creation are not included unless you add them.

Credential isolation

Your Hunch 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 'classify short texts' or 'score reviews on a scale', and Jentic returns the matching Hunch operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Hunch API through Jentic.

What authentication does the Hunch API use?

Per its OpenAPI spec, the Hunch API uses a Hunch key sent as a bearer token, in the form hunch_ followed by 40 hex characters. POST /trial needs no key and returns one with 100 free rows, and GET /v1/quote works with or without a key. Through Jentic the key is stored encrypted by your own Jentic One instance and injected per call.

How is the Hunch API priced?

Hunch's spec states one credit per answered text, with blank texts and repeats inside a call free, and one credit per question per text for the multi kind. After the 100 free trial rows, the spec lists $29 for 5,000 rows and $19 for 25,000 more, with no subscription. GET /v1/quote prices a job for free before you run it.

What are the rate limits for the Hunch API?

The OpenAPI spec does not specify request rate limits for the ask endpoint; each call is capped at 40 rows, and POST /trial is limited to 2 keys per address per day. Check https://hunchsheet.app for current limits.

What happens when a Hunch key runs out of credits?

By default POST /v1/ask returns 200 with per-row errors. If you send the optional X-Hunch-Agent header with your agent or product name, it instead answers 402 with a checkout offer. POST /v1/checkout returns a Stripe link that a person must open and pay; the spec states an agent cannot pay for them.

How do I classify texts with the Hunch API through Jentic?

Search Jentic for 'classify short texts' to get POST /v1/ask, then send kind choice with your options and up to 40 rows, splitting larger sets across calls. To run it on your own infrastructure, install Jentic One from its GitHub repo and store your Hunch key once.

Is there a Hunch MCP server?

Yes. Hunch's own spec says the same engine is available as an MCP server. You can also connect the Hunch API directly through Jentic: your agent gets the full 5-endpoint surface without loading another server's tool definitions into its context, your key 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 Hunch API?

Yes. For example, write a rule that allows only GET /v1/quote, GET /v1/balance, and POST /v1/ask, so the agent can price, run, and track jobs but cannot mint trial keys or create checkout links. Every call it makes is logged by your Jentic One instance.

Hunchsheet App Hunch API

AI ReadinessAI-Aware (B+)67/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLMl Inferencebearer5 EndpointsREST

For Agents

Classify, score, or yes/no-judge up to 40 short texts per call and get one typed result per text, with free quoting, balance checks, a self-serve 100-row trial key, and checkout links for more credits.

Use for: Sort these 300 support tickets into billing, refund, bug, or other, Score each review from angry to delighted, Check whether each lead is a decision maker, How much would judging 5,000 rows with 3 questions cost

Not supported: Does not generate free-form text, summarise documents, or train custom models. Use for typed judgements on batches of short texts only.

The Hunch API judges batches of short texts against a question and returns one typed answer per text instead of generated prose. Each call takes up to 40 texts and one of four kinds: a yes/no probability, a pick from up to 255 options, a position on a 2 to 10 level scale, or one probability for each of up to 10 yes/no questions. Billing is one credit per answered text, with blank texts and repeats inside a call free, and a free quote call prices a job before it runs. An agent can mint its own 100-row trial key, check the credits left on a key, and hand a person a Stripe checkout link to add more.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

100

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).

100%

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.

55%

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: C-Signals: 4

33%

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.

67%

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

68%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Hunch API to your agent

Capabilities

Start building with Hunch API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Hunchsheet App Hunch API

Jentic AI Readiness Score

Hunch 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