MessageBird FAQ
Browse all Messagebird APIs★ Only Publicly Available OpenAPI DocumentAI/MLLanguage ModelsapiKey4 EndpointsREST
For Agents
Predict the best FAQ answer to a user question against a curated MessageBird dataset across multiple languages.
Use for: I need to predict the best FAQ answer for a user question, List the FAQ datasets configured in MessageBird, Retrieve a specific FAQ dataset, Check which languages MessageBird FAQ supports
Not supported: Does not handle SMS or voice messaging, free-form text generation, or dataset authoring — use for FAQ dataset retrieval and answer prediction only.
Jentic publishes the only available OpenAPI specification for MessageBird FAQ, keeping it validated and agent-ready. The MessageBird FAQ API hosts curated question-and-answer datasets and predicts the best matching answer for an end-user question against a chosen dataset. It exposes dataset listing and retrieval, an answer-prediction endpoint that scores candidate answers from the dataset, and a supported-languages lookup, making it a focused building block for FAQ-driven chat and IVR experiences inside the wider MessageBird platform.
Install Jentic One Beta
Connect the MessageBird FAQ to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the MessageBird FAQ, 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.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic 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 MessageBird FAQ API.
List FAQ datasets configured for the MessageBird account
Retrieve a specific FAQ dataset by ID for inspection or content syncing
Predict the best matching answer for a free-text user question against a chosen dataset
Look up which languages are supported for FAQ prediction before routing a query
Power chatbot and IVR flows with grounded FAQ responses rather than free-form generation
Use Cases
Patterns agents use MessageBird FAQ API for, with concrete tasks.
★ FAQ-Grounded Chatbot
Customer-support chatbots can ground their responses in a curated FAQ dataset by calling POST /api/v1/datasets/{datasetId}/predict-answer with the user's question. The endpoint returns the best matching answer from the dataset along with confidence information, so the bot can reply with a vetted answer or escalate when the score is too low.
Call POST /api/v1/datasets/{datasetId}/predict-answer with the customer's question and serve the predicted answer if its confidence exceeds the configured threshold.
IVR Self-Service
Voice teams running IVR flows on top of MessageBird can use the FAQ API to handle natural-language questions captured by speech-to-text. A successful prediction lets the IVR speak the FAQ answer back; a low-confidence response triggers transfer to a human agent. The supported-languages endpoint helps route per-locale FAQs.
Call GET /api/v1/supported-languages, then POST /api/v1/datasets/{datasetId}/predict-answer for the caller's locale to drive the IVR self-service step.
Knowledge Base Sync
Internal knowledge platforms can keep their content aligned with the MessageBird FAQ datasets by listing datasets via GET /api/v1/datasets and pulling individual datasets via GET /api/v1/datasets/{datasetId}. This supports a publishing workflow where editors maintain answers in a CMS and downstream chat or voice channels read from the synced dataset.
Call GET /api/v1/datasets to list datasets, then GET /api/v1/datasets/{datasetId} to pull the active dataset into the internal knowledge base.
Agent-Driven Customer Support
An AI agent fielding inbound customer messages can call the MessageBird FAQ API via Jentic to ground its replies in vetted answers. The agent searches Jentic for the predict-answer operation, loads the schema, and posts the user's question without writing custom MessageBird client code or handling raw access keys.
Search Jentic for 'predict the best FAQ answer with MessageBird', load POST /api/v1/datasets/{datasetId}/predict-answer, and execute it with the inbound user question.
Key Endpoints
4 endpoints — jentic publishes the only available openapi specification for messagebird faq, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
GET
/api/v1/datasets
List FAQ datasets
GET
/api/v1/datasets/{datasetId}
Retrieve a specific FAQ dataset
POST
/api/v1/datasets/{datasetId}/predict-answer
Predict the best matching answer
GET
/api/v1/supported-languages
List supported languages
GET
/api/v1/datasets
List FAQ datasets
GET
/api/v1/datasets/{datasetId}
Retrieve a specific FAQ dataset
POST
/api/v1/datasets/{datasetId}/predict-answer
Predict the best matching answer
GET
/api/v1/supported-languages
List supported languages
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
MessageBird AccessKeys are stored in the Jentic vault. Agents authenticate with a scoped Jentic token and Jentic attaches the AccessKey at the gateway, so the raw MessageBird key never enters the agent context.
Intent-based discovery
Agents search Jentic with intents like 'predict an FAQ answer' and receive POST /api/v1/datasets/{datasetId}/predict-answer with the dataset ID and question fields modelled in the schema.
Time to first call
Direct MessageBird FAQ integration: 1-2 days across dataset listing, prediction, and supported-language routing. Through Jentic: under an hour.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using MessageBird FAQ API through Jentic.
Why is there no official OpenAPI spec for MessageBird FAQ?
MessageBird does not publish an OpenAPI specification for the FAQ product. Jentic generates and maintains this spec so that AI agents and developers can call MessageBird FAQ via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.
What authentication does the MessageBird FAQ API use?
The FAQ API uses a MessageBird AccessKey passed in the Authorization header. Through Jentic, that AccessKey lives in the credential vault and the agent calls the API with a scoped Jentic token, so the raw key never enters the agent context.
Can I get a confidence score with the predicted answer?
Yes. POST /api/v1/datasets/{datasetId}/predict-answer returns the best-matching answer from the dataset along with score information so callers can apply a confidence threshold and fall back to human handoff when the match is weak.
What are the rate limits for the MessageBird FAQ API?
The OpenAPI spec does not declare explicit rate limits. MessageBird applies account-level throttling on FAQ endpoints; high-volume integrations should batch retrieval calls and back off on 429 responses rather than calling predict-answer in a tight loop.
How do I predict an FAQ answer through Jentic?
Install the SDK with pip install jentic, search for 'predict the best FAQ answer with MessageBird', load the POST /api/v1/datasets/{datasetId}/predict-answer operation, and execute it with the dataset ID and the user question. Jentic returns the matched answer and score.
Which languages does the MessageBird FAQ API support?
Call GET /api/v1/supported-languages to retrieve the current list. The dataset configuration determines which of those languages a specific FAQ can be queried in, so route queries by checking the dataset's locale before predicting.
GET STARTED