Agent-ready OpenAPI document · curated by JenticAI/MLMl InferenceapiKey4 EndpointsREST
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For Agents
Classify customer messages into trained intent labels by calling a MessageBird Intent dataset, with confidence scores returned per prediction.
Use for: Classify this customer message into a support intent, List all intent datasets available to my account, Get the training status of a specific intent dataset, Predict the intent of an inbound SMS using my support classifier
Not supported: Does not handle message delivery, language detection, or entity extraction - use for intent classification of short text only.
Jentic publishes the only available OpenAPI specification for Intent, keeping it validated and agent-ready. The MessageBird Intent API classifies short text utterances into customer-defined intents using trained classification models that live in datasets. Agents send a phrase to a chosen dataset and receive predicted intent labels with confidence scores, which is useful for routing inbound SMS, WhatsApp messages, or chatbot turns to the right workflow. Supported languages are exposed via a dedicated endpoint, and dataset metadata reveals training status before predictions are attempted.
Install Jentic One Beta
Connect the Intent to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Intent, 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://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmessagebird.com%2Fmessagebird-intent-api" | sh2
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%2Fmessagebird.com%2Fmessagebird-intent-api" | 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 Intent API.
Classify an inbound SMS phrase into a trained intent label and read its confidence score
List all intent classification datasets available on the account before choosing one for prediction
Inspect a single dataset to confirm its training status before sending production traffic
Look up the languages supported by the Intent service to gate predictions on unsupported locales
Route customer support replies into automated workflows based on the predicted intent label
Use Cases
Patterns agents use Intent API for, with concrete tasks.
★ Inbound Support Triage
Classify inbound SMS or chat messages into a fixed set of support intents (billing, cancellation, technical issue) so each conversation is routed to the correct queue or automated reply. The Intent API runs the prediction against a dataset that was previously trained on labelled examples and returns the top intent label with a confidence score, letting the workflow fall back to a human when confidence is low. Triage decisions can be made in milliseconds before the message ever reaches an agent.
POST a customer message to /api/v1/datasets/{datasetId}/predict on the support classifier dataset and forward it to the billing queue when the predicted intent is 'billing_question' with confidence above 0.8
Chatbot Disambiguation
Use predicted intents to drive branching logic inside an automated chat flow. When a user replies in free text, the bot calls the Intent API with the user's phrase and uses the returned label to pick the next step, instead of relying on brittle keyword matching. Datasets can be retrained as new intents emerge without changing the bot code, and the supported-languages endpoint lets the bot decline gracefully on unsupported locales.
Call POST /api/v1/datasets/{datasetId}/predict with the user's chat reply and branch the flow based on the predicted intent label
Dataset Readiness Monitoring
Before promoting a new classifier into production traffic, agents can list all datasets and inspect each one individually to confirm training has finished. The dataset detail endpoint exposes status information so a deployment workflow can wait for training to complete before flipping traffic. This avoids the failure mode of sending live messages to a half-trained model.
GET /api/v1/datasets and then GET /api/v1/datasets/{datasetId} to verify training has completed before routing live traffic to the classifier
AI Agent Integration via Jentic
An AI agent uses Jentic to discover the Intent API by intent search ('classify a customer message') without needing to read MessageBird's documentation. Jentic returns the predict operation with its input schema, so the agent can submit a phrase and receive a structured intent prediction in a single load-and-execute step. Credentials are isolated in your Jentic One instance and never enter the agent's prompt.
Use Jentic to search 'classify a customer message into intents', load the Intent predict operation schema, and execute it against the production support dataset
Key Endpoints
4 endpoints — jentic publishes the only available openapi specification for intent, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
GET
/api/v1/datasets
List all intent datasets
GET
/api/v1/datasets/{datasetId}
Get a single dataset including training status
POST
/api/v1/datasets/{datasetId}/predict
Predict the intent of a phrase against a dataset
GET
/api/v1/supported-languages
List supported classification languages
GET
/api/v1/datasets
List all intent datasets
GET
/api/v1/datasets/{datasetId}
Get a single dataset including training status
POST
/api/v1/datasets/{datasetId}/predict
Predict the intent of a phrase against a dataset
GET
/api/v1/supported-languages
List supported classification languages
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
I
Intent
- Foundational (C)54/100
73
Foundational Compliance77
Developer Experience & Jentic Compatibility29
AI-Readiness & Agent Experience94
Agent Usability50
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
73
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A-Signals: 4
90%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
0%
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.
77
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: A-Signals: 4
35%
Example Density
How richly the API is illustrated with examples.
96%
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.
29
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
64%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
0%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
50%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
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.
50
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).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the MessageBird Intent API by hand means attaching your AccessKey to each request against the intent.messagebird host, routing dataset lookups and the predict call, and handling retries yourself. Through Jentic you install once, import the Intent API from the API Directory, store the AccessKey once, and your agent calls it.
Permission scoping
The Intent API puts the dataset id in the URL path (/api/v1/datasets/{datasetId}/predict), so a rule can pin your agent to one dataset. You choose the operations it may call, so a rule can grant dataset reads and intent prediction while leaving out anything else.
Credential isolation
Your MessageBird AccessKey is stored once, encrypted, by your own Jentic One instance and attached at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'classify a customer message', and Jentic returns the Intent predict operation with its input schema so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Intent API through Jentic.
Why is there no official OpenAPI spec for Intent?
MessageBird does not publish an OpenAPI specification for the Intent API. Jentic generates and maintains this spec so that AI agents and developers can call Intent via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.
What authentication does the MessageBird Intent API use?
The Intent API uses an API key sent in the `Authorization` header, prefixed with the literal word `AccessKey`. Through Jentic, the access key is stored in your Jentic One instance and injected at request time, so the raw key never enters the agent's context.
Can I list available intent datasets before running predictions?
Yes. Call GET /api/v1/datasets to enumerate every dataset on the account, then GET /api/v1/datasets/{datasetId} for full details on a specific one. This is how you confirm a model has finished training before sending live traffic to it.
What languages does the Intent API support?
Call GET /api/v1/supported-languages to retrieve the full list at runtime rather than hardcoding it. The set evolves as MessageBird adds language coverage to the underlying classifier.
How do I classify a message through Jentic?
Search Jentic for `classify a customer message into intents`, load the POST /api/v1/datasets/{datasetId}/predict operation, and execute it with your phrase and target dataset. Jentic returns the predicted intent label plus confidence in a structured response.
Are there rate limits on the Intent API?
The OpenAPI spec does not document specific quantitative rate limits, so check your MessageBird account dashboard or contact MessageBird support for the limits attached to your plan before bulk-classifying traffic.
Can I limit what my agent is allowed to do with the MessageBird Intent API?
Yes. Because you run Jentic One yourself, your own rules decide which Intent operations and credentials your agent may use. You can allow only dataset reads (GET /api/v1/datasets and GET /api/v1/datasets/{datasetId}) plus the predict call (POST /api/v1/datasets/{datasetId}/predict) and leave out anything else. Since the dataset id sits in the URL path, a rule can also pin the agent to a single classifier dataset so it cannot run predictions against others.
GET STARTED