How do I get a Deep Talk API Key?

Log in to your Deep Talk account, navigate to the API section in your settings, and you can generate or retrieve your unique API Key from there.

Can the agent process real-time conversations?

This integration currently focuses on analyzing datasets that have already been uploaded and processed within Deep Talk. Real-time streaming analysis is managed via the Deep Talk dashboard or webhook integrations.

What languages are supported for analysis?

Deep Talk supports multiple languages for NLP analysis, including English, Spanish, Portuguese, and French. The agent retrieves results based on the analysis performed in your account.

Deep Talk MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: Deep Talk
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Deep Talk through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Deep Talk Assistant",
            instructions=(
                "You help users interact with Deep Talk. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Deep Talk"
        )
        print(result.final_output)

asyncio.run(main())

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About Deep Talk MCP Server

Integrate Deep Talk, the powerful conversation analysis platform, directly into your AI workflow. Process large-scale conversation data from sources like Intercom or Zendesk, extract key topics and clusters, and analyze sentiment trends using natural language.

The OpenAI Agents SDK auto-discovers all 10 tools from Deep Talk through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Deep Talk, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Dataset Oversight — List and retrieve metadata for all your uploaded conversation datasets and their processing status.
Topic Extraction — Identify key themes and extracted topics from your conversation data automatically.
Sentiment Analytics — Retrieve summaries of sentiment across your entire customer interaction database.
Conversation Clustering — List clusters of similar conversations identified by Deep Talk's NLP models.

The Deep Talk MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Deep Talk to OpenAI Agents SDK via MCP

Follow these steps to integrate the Deep Talk MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from Deep Talk

Why Use OpenAI Agents SDK with the Deep Talk MCP Server

OpenAI Agents SDK provides unique advantages when paired with Deep Talk through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Deep Talk + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Deep Talk MCP Server delivers measurable value.

Automated workflows: build agents that query Deep Talk, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Deep Talk, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Deep Talk tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Deep Talk to resolve tickets, look up records, and update statuses without human intervention

Deep Talk MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect Deep Talk to OpenAI Agents SDK via MCP:

get_account_details

Returns account-level metadata such as subscription tier, remaining processing credits, and user roles. Retrieve metadata and usage limits for your Deep Talk account

get_dataset_metadata

Resolves creation dates, source integrations, and whether NLP clustering has completed. Get metadata and processing status for a specific dataset

get_sentiment_analytics

Returns a distribution of positive, neutral, and negative sentiment scores across the dataset records. Retrieve a summary of sentiment across the entire dataset

list_analysis_datasets

Returns dataset metadata including names, record counts, and current processing status for NLP analysis. List all conversation datasets uploaded for analysis

list_available_nlp_models

g., sentiment, intent, clusterers) that can be applied to datasets for analysis. List NLP models available for conversation categorization

list_connected_sources

Returns a list of connected external platforms, their synchronization status, and the volume of data ingested from each. List external data sources (e.g. Zendesk, Intercom) connected to Deep Talk

list_conversation_clusters

Returns groups of semantically similar conversations identified through unsupervised learning, including cluster sizes and representative keywords. List clusters of similar conversations identified in a dataset

list_extracted_topics

Returns a list of identified themes with their respective prevalence and importance scores within the specified dataset. List key topics and themes extracted from the conversation data

list_processing_tasks

Returns a list of active processing jobs, including ingestion and NLP analysis tasks, and their current completion percentages. List current data processing and analysis tasks

search_topics_by_keyword

Identifies and returns themes that match the provided search term. Search for specific topics or themes within a dataset

Example Prompts for Deep Talk in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Deep Talk immediately.

"List all conversation datasets currently processed."

"Show me the top topics identified in the 'Customer Feedback' dataset."

"What is the sentiment summary for our recent support interactions?"

Troubleshooting Deep Talk MCP Server with OpenAI Agents SDK

Common issues when connecting Deep Talk to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Deep Talk + OpenAI Agents SDK FAQ

Common questions about integrating Deep Talk MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.