add_agent
Add a new agent to the Enkrypt AI MCP Server by configuring its endpoint URL, custom headers, payload, and tools for testing and integration purposes.
Instructions
Add a new agent using the provided configuration.
Args: config: A dictionary containing the agent configuration details. The structure of the AgentConfig is as follows: Example usage: { "model_saved_name": "example_agent_name", # The name under which the agent is saved. "model_version": "v1", # The version of the agent. "testing_for": "agents", # The purpose for which the agent is being tested. (Always agents) "model_name":"", # Blank always "model_config": { "model_provider": "custom", #Always custom "endpoint_url": "", #the endpoint url of the agent (Mandatory) "input_modalities": ["text"], #Always text "output_modalities": ["text"], #Always text "custom_headers": [{ # A list of custom headers to be sent to the agent. (Mandatory) "key": "Content-Type", "value": "application/json" }...], "custom_response_format": "", # Ask user for the response format of the agent in jq format (Mandatory) "custom_response_content_type": "json", # The content type of the agent's response (always json) (Mandatory) "custom_payload", # Ask user for the payload to be sent to the agent (always keep placeholder for prompt as '{prompt}') (Mandatory) "tools": [{ # Ask user for a list of tools to be used by the agent. (MANDATORY) "name": "name of the tool", "description": "description of the tool" }...] }, } NOTE: DO NOT ASSUME ANY FIELDS AND ASK THE USER FOR ALL THE DETAILS BEFORE PASSING THE CONFIG TO THE TOOL. Ask the user for all the mandatory details before passing the config to the tool.
Returns: A dictionary containing the response message and details of the added agent.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| config | Yes |