Metatext AI specializes in natural language processing and text generation, helping organizations automate writing tasks, sentiment analysis, or content moderation
Metatext AI specializes in natural language processing and text generation, helping organizations automate writing tasks, sentiment analysis, or content moderation On Nagent, Metatextai is exposed as a fully-configurable artificial intelligence integration that any agent can call — 13 actions, and API key authentication. No code is required to wire Metatextai into your workflow — connect it once via the External Integrations panel and reuse it across every agent you build.
Agent builders use Metatextai to automate the kinds of tasks artificial intelligence teams previously handled manually. Concrete examples — each one is a single agent step in Nagent — include:
Every action and trigger is paired with a structured input/output schema (visible in the sections below), so when you wire Metatextai into Helix — our agentic agent builder — the editor knows exactly what each step expects and produces. Configure once, deploy anywhere across your Nagent agents.
Every operation an agent can call against Metatextai, with input parameters and output schema. Drop these into any step of an agent built in Helix.
METATEXTAI_CHAT_COMPLETIONSTool to generate chat completions. Use when you need OpenAI-compatible conversational responses.
Input parameters
Optional stop sequence to end generation when encountered.
Model to use for chat completion.
Optional prompt text; alternative to using 'messages'.
Optional project identifier for billing or project-specific settings.
List of message objects for conversation; alternative to 'prompt'.
Maximum number of tokens to generate.
Sampling temperature to use, between 0.0 and 2.0.
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_CLASSIFYTool to classify text. Use when you need to obtain labels and confidence scores from a trained MetatextAI model for given text.
Input parameters
Text to classify.
Model identifier to use. Defaults to the project's primary model if omitted.
Optional settings to refine classification behavior.
Identifier of the project to use for classification.
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_CREATE_POLICY_GUARDRAILSTool to create a policy guardrail. Use when you need to define automated guardrails for content in a specific application.
Input parameters
Unique identifier for the new policy
List of policy rules to enforce
Where the policy applies. Allowed values: 'user', 'assistant', 'context', 'system'. Aliases: 'output'=assistant, 'input'=\['user','context'\]
Optional short description of the policy
Identifier of the application in which to create the policy
Optional custom message returned when a policy violation occurs
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_DELETE_POLICY_GUARDRAILSTool to delete a guardrail policy. Use when you need to remove a policy by ID for a specific application after confirming valid application and policy IDs.
Input parameters
The ID of the policy to delete.
The ID of the application.
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_EVALUATETool to evaluate LLM messages against policies/guardrails. Use after generating model output to get violation details or corrections.
Input parameters
Conversation messages to evaluate. Must include 'user' and 'assistant' messages; 'system' optional.
Inline policies to evaluate; overrides console defaults when provided.
If true, stops evaluation on first violation.
List of policy IDs to override default console policies.
Application identifier; defaults to your configured application ID.
Top-level override response message on violation. Overrides correction.
If true, returns corrected output when violations occur (unless override_response is set).
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_EXTRACTTool to run information extraction. Use when you need to extract structured data from text.
Input parameters
Text to perform information extraction on
Optional model identifier to use for extraction
Additional options for extraction
Identifier of the project to run extraction on
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_GENERATETool to generate text for a project model. Use when you need LLM completions or chat responses. Supports both prompt and message-based inputs with temperature, stop-sequence, and token limits.
Input parameters
Sequence at which to stop generating further tokens.
Model to use for generation (e.g., 'gpt-3.5').
Prompt text for completion. Provide this OR `messages`, not both.
List of chat messages for chat-based models. Provide this OR `prompt`, not both.
Maximum number of tokens to generate.
Identifier of the project to use for generation.
Sampling temperature between 0 and 2.
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_LIST_APPLICATIONSTool to retrieve a list of all existing applications. Use when you need to view application IDs, names, and descriptions.
Input parameters
Filter by tag or label (if supported)
Maximum number of applications to return (if supported)
Number of items to skip (if supported)
Filter applications by name/description (if supported)
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_LIST_MODELSTool to retrieve a list of all available models and their supported tasks. Use when you need to choose an appropriate model for chat completions.
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_LIST_POLICIES_GUARDRAILSTool to list all guardrail policies for a specific application. Use after obtaining an application ID to inspect its configured policies.
Input parameters
Identifier of the application to list policies for
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_LIST_RED_TEAM_TEST_PROBESTool to list all available red team test probes. Use when you need to discover available probes for red teaming.
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_RUN_RED_TEAM_TEST_SCANTool to run a vulnerability red-team test scan. Use when you need to execute probes against an application.
Input parameters
List of probe identifiers to run. If not provided, default probes will be used.
The application ID to scan.
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
METATEXTAI_UPDATE_POLICY_GUARDRAILSTool to update an existing policy's guardrails. Use when you need to modify a policy's rules after confirming it exists.
Input parameters
Unique policy ID. Must be identical to the path `policy_id`.
List of rule objects that define the guardrails.
Where rules apply. Allowed values: 'user', 'assistant', 'context', 'system'. Alias 'input' maps to \['user','context'\], 'output' maps to 'assistant'.
The policy ID to update. Must match `id` in the body.
Short description of the policy (optional).
The application ID that scopes the policy.
Custom message returned when violations occur (optional).
Output
Data from the action execution
Error if any occurred during the execution of the action
Whether or not the action execution was successful or not
No publicly available marketplace agent is found using this tool yet. There are 64 agents privately built on Nagent that already use Metatextai.
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Connect Metatextai to any Nagent agent in minutes — no API key management, no boilerplate. Just configure and deploy.
The five questions agent builders ask before adopting a new integration.
Open the External Integrations panel inside Nagent (app.nagent.ai/externalIntegration), find Metatextai, and click "Connect Now." You'll authenticate with an API key — Nagent handles credential storage and refresh automatically. Once connected, Metatextai is available to any agent in your workspace.
No. Nagent provides no-code integration for every tool. Once Metatextai is connected, you configure its 13 actions directly in the agent builder UI — no API calls, no boilerplate, no schema management.
Helix — Nagent's agentic agent builder — lets you drop Metatextai steps into any workflow visually. Pick an action (e.g., one of those listed above), fill in the inputs (Helix knows the required vs. optional schema for each parameter), and connect it to upstream/downstream steps. Triggers run as the entry point of an agent, so when a Metatextai event fires, the agent kicks off automatically.
Every Metatextai action and trigger ships with a fully-typed schema — input parameters with name, type, required flag, and description, plus the output payload shape. The schemas are documented in the sections above. Helix uses these schemas to validate your configuration at build time and to type-check the data flowing between steps.
Yes. While Metatextai ships with 13 pre-built artificial intelligence actions, you can layer custom logic around them inside Helix — pre/post-processing steps, conditional branches, retries, or stitching Metatextai together with other connected tools. For deeper customization, talk to our team about Nagent's Agentic AI Lab — forward-deployed engineers who build Metatextai-based workflows tailored to your business.