Install Jentic One Beta

Connect the Chat Data API Schema Guide to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Chat Data API Schema Guide, 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%2Fchat-data.com%2Fchat-data" | 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%2Fchat-data.com%2Fchat-data" | 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 Chat Data API Schema Guide API.

Create and train an AI chatbot on documents, links, and Q&As

Send a message to a chatbot and receive its grounded reply

Query a chatbot's knowledge base and score answer confidence

Retrain a chatbot after its source material changes

Read conversations and attach leads and customers to them

GET STARTED

Start building with Chat Data API Schema Guide API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Chat Data API Schema Guide API for, with concrete tasks.

★ AI Agent Chatbot Orchestration

An AI agent connected through Jentic can stand up a support bot end to end: it creates a chatbot from a set of document URLs, checks the training status until it is ready, and sends a test message to confirm the bot answers. Chat Data handles ingestion and retrieval so the agent orchestrates setup rather than wiring each step.

Create a chatbot from a list of document URLs, poll its training status until ready, and send a test message to confirm it answers

Support Chatbot on Your Docs

Answer customer questions from your own content by training a chatbot on documents, website links, and Q&As. Chat Data grounds replies in that knowledge base and returns a confidence score, so a workflow can escalate low-confidence answers to a human.

Query the chatbot's knowledge base for a customer question and return the answer with its confidence score

Omnichannel Live Chat

Hand off from bot to human and reach users on messaging channels by running live chat and sending outbound WhatsApp messages. Chat Data toggles bot activation per conversation so a workflow can pause the bot when an agent takes over.

Pause the bot in a conversation, send a live-chat reply, then send an outbound WhatsApp message to the user

Conversation and Lead Capture

Turn chats into pipeline by reading conversations and attaching leads and customers to them. Chat Data exposes conversation history and lead attachment so captured contacts flow to downstream systems.

Retrieve a conversation by its identifier and attach a lead with the captured contact details

Key Endpoints

43 endpoints — the chat data api builds ai chatbots trained on your own documents, website links, and q&as, then serves their answers over chat, live chat, and whatsapp.

METHOD

PATH

DESCRIPTION

POST

/create-chatbot

Create and train a chatbot

POST

/chat

Send a message to a chatbot

POST

/query-knowledge-base/{chatbotId}

Query the knowledge base

GET

/get-chatbots

List all chatbots

POST

/retrain-chatbot

Retrain an existing chatbot

GET

/get-conversations/{chatbotId}

Get conversations for a chatbot

POST

/add-webhook

Add a webhook

POST

/create-chatbot

Create and train a chatbot

POST

/chat

Send a message to a chatbot

POST

/query-knowledge-base/{chatbotId}

Query the knowledge base

GET

/get-chatbots

List all chatbots

POST

/retrain-chatbot

Retrain an existing chatbot

GET

/get-conversations/{chatbotId}

Get conversations for a chatbot

POST

/add-webhook

Add a webhook

Why Jentic?

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

Setup

Wiring Chat Data by hand means handling its bearer-token auth and tracking the chatbot, knowledge-base, conversation, and live-chat paths yourself. Through Jentic you install once, import Chat Data from the API Directory, store the token once, and your agent calls it.

Permission scoping

You choose which Chat Data operations the agent may call, such as sending messages and querying the knowledge base, so operations like deleting chatbots or conversations are not included unless you add them.

Credential isolation

Your Chat Data token 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 'send a chatbot message' or 'query the knowledge base', and Jentic returns the matching Chat Data operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Chat Data API Schema Guide API through Jentic.

What authentication does the Chat Data API use?

The Chat Data API authenticates with a bearer token sent in the Authorization header, per its OpenAPI spec. You create the token in your Chat Data account, and through Jentic it is stored encrypted by your own Jentic One instance and injected at call time, so it never appears in the agent's prompt or logs.

Can I train a chatbot on my own data with the Chat Data API?

Yes. You create a chatbot from documents, website links, plain text, or Q&As, then retrain it when the sources change. The API reports training status and lets you query the resulting knowledge base with a confidence score on each answer.

Can I run live chat and WhatsApp with the Chat Data API?

Yes. The API sends and receives chat messages, runs live chat with bot control per conversation, and sends outbound WhatsApp messages. You can pause or resume the bot in a conversation when a human agent takes over.

Is there a Chat Data MCP server?

You do not need an MCP server to give your agent the Chat Data API. Jentic connects it directly from the API Directory: import Chat Data, store your token once, and your agent creates chatbots and answers messages through the operations the API already exposes. Operations are discovered on demand, so no extra tool definitions are loaded into the agent's context.

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

Yes. Write a rule that allows only the operations the agent needs, for example sending messages and querying the knowledge base, and leave out operations like deleting chatbots or conversations. The agent serves answers within those bounds, and every call it makes is logged.

How do I create a chatbot through Jentic with the Chat Data API?

Search Jentic for 'create and train a chatbot', which returns the Chat Data operation with its input schema, then supply the source documents and settings and the agent runs it. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Chat Data API Schema Guide

AI ReadinessAI-Aware (B)65/100

See full scorecard

Official vendor OpenAPI document · agent-readyCommunicationsChat Messagingbearer43 EndpointsREST

For Agents

Create and train custom AI chatbots on your data, send and receive chat messages, query the knowledge base, and manage conversations through the Chat Data API.

Use for: Create and train a chatbot on my documents, Send a message to my chatbot and get its reply, Query my chatbot's knowledge base, Retrain a chatbot after updating its sources

Not supported: Does not handle raw language-model hosting, voice calls, or email campaigns. Use for building and serving AI chatbots trained on your own data only.

The Chat Data API builds AI chatbots trained on your own documents, website links, and Q&As, then serves their answers over chat, live chat, and WhatsApp. It queries a chatbot's knowledge base, scores answer confidence, and records conversations with attached leads and customers. Chatbot settings, white-label options, webhooks, and sub-users round out the platform.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: C+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).

42%

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: A-Signals: 4

27%

Example Density

How richly the API is illustrated with examples.

99%

Example Validity

Percentage of examples that conform to their schemas.

59%

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

64%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

93%

OperationId Quality

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

48%

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: A-Signals: 1

80%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Chat Data API Schema Guide to your agent

Capabilities

Start building with Chat Data API Schema Guide API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Chat Data API Schema Guide

Jentic AI Readiness Score

Chat Data API Schema Guide

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