Built by Metorial, the integration platform for agentic AI.
List all entities configured in the Wit.ai app, including both built-in and custom entities. Returns entity names and IDs.
List all Wit.ai apps accessible by the current token. Returns app metadata including name, language, and training status. Supports pagination.
Create a new version tag for a Wit.ai app. Call list_apps to discover accessible app IDs. Tags snapshot the current NLU model state for versioning.
Get detailed information about a specific trait, including its possible values.
Add or remove possible values for an existing trait.
Permanently delete an intent from the Wit.ai app. This cannot be undone.
Submit training utterances (samples) to train the Wit.ai NLU model. Each utterance can include intent labels, entity annotations with character positions, and trait annotations. Useful for bulk importing training data.
Permanently delete an entity from the Wit.ai app. This cannot be undone.
Create a new custom entity in the Wit.ai app. Entities can be keyword-based (with explicit values) or free-text. Specify roles to distinguish how the entity is used in different contexts (e.g., "origin" vs "destination" for a location entity).
Permanently delete a Wit.ai app. Call list_apps to discover accessible app IDs. This action cannot be undone.
Extract meaning from text using Wit.ai's NLU engine. Returns detected **intents**, **entities**, and **traits** with confidence scores. Supports optional context (locale, timezone, reference time) for improved entity resolution. Use the `maxResults` parameter to get multiple candidate intents (N-best).
Export the current Wit.ai app configuration as a downloadable ZIP file containing its intents, entities, traits, utterances, and settings.
List all traits configured in the Wit.ai app. Traits are sentence-level classifiers (e.g., sentiment, politeness) that are distinct from entities.
Create a new trait in the Wit.ai app with initial values. Traits are sentence-level classifiers such as sentiment, greeting detection, or custom categories.
Get detailed information about a specific Wit.ai app by its ID. Call list_apps to discover accessible app IDs. Returns configuration, training status, and metadata.
Delete a version tag from a Wit.ai app. Call list_apps to discover accessible app IDs. This cannot be undone.
List all intents configured in the Wit.ai app. Returns intent names and IDs. Use this to explore available intents before analyzing text.
Get detailed information about a specific intent, including its associated entities.
Update an existing Wit.ai app's configuration such as name, language, timezone, description, or privacy settings. Call list_apps to discover accessible app IDs.
Get detailed information about a specific entity, including its roles, lookup strategies, and keyword values.
Create a new Wit.ai app. Returns the new app's ID and server access token that can be used to interact with the app.
List training utterances (samples) in the Wit.ai app. Returns annotated utterances with their intent, entity, and trait labels. Supports pagination.
Delete training utterances from the Wit.ai app by their text content. Useful for cleaning up or correcting training data.
Permanently delete a trait from the Wit.ai app. This cannot be undone.
Update an existing entity's configuration, such as its name, roles, lookup strategies, or keywords.
List available voices for text-to-speech synthesis. Returns voice names, locales, genders, and available styles.
Detect the language of a given text. Returns the N-best detected locales with confidence scores. Useful for routing multilingual inputs or selecting the correct NLP model.
Create a new intent in the Wit.ai app. After creation, train the intent by adding utterances.
Convert text to a downloadable audio file using a Wit.ai voice. Call list_voices to choose a supported voice and style.
List all version tags for a Wit.ai app. Call list_apps to discover accessible app IDs. Tags provide version control for your NLU models, allowing you to snapshot and reference specific model versions.
Add or remove keywords and synonyms for a keyword-type entity. Use this to expand the vocabulary of an entity by adding new canonical keywords with their synonym expressions, or to remove existing ones.