Deep Talk MCP. Analyze customer conversations by topic and sentiment.
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Deep Talk analyzes conversation datasets to extract key topics, measure sentiment, and cluster similar interactions. It lets your AI agent pull metadata, list connected sources (Zendesk, Intercom), and run sentiment analytics across massive amounts of customer support data.
Use it to find exactly what customers are talking about, the pain points, and how they feel about it.
What your AI agents can do
Get account details
Returns usage limits and metadata about your Deep Talk account.
Get dataset metadata
Gets the creation date, source integration, and processing status for a specific dataset.
Get sentiment analytics
Retrieves a summary of positive, neutral, and negative sentiment scores across the entire dataset.
Retrieves usage limits and metadata about your Deep Talk account.
Resolves the creation dates, source integrations, and completion status for a specific conversation dataset.
Returns a breakdown of positive, neutral, and negative sentiment scores across all records in a dataset.
Retrieves metadata for every conversation dataset you've uploaded for analysis, including record counts and status.
Shows which models (like sentiment, intent, or clusterers) you can apply to your conversation data.
Returns a list of external platforms (e.g., Zendesk, Intercom) and how much data has been pulled from each.
Returns groups of conversations that are semantically similar, including cluster sizes and representative keywords.
Lists key themes and topics, showing their prevalence and importance scores within a dataset.
Lists active jobs, including ingestion and NLP analysis tasks, and their current completion percentages.
Identifies and returns themes that match a specific search term you provide.
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Deep Talk MCP Server: 10 Tools for Conversation Insights
Use these tools to analyze conversation data, list topics, check sentiment, and monitor the status of customer support datasets.
019d7583get account details
Returns usage limits and metadata about your Deep Talk account.
019d7583get dataset metadata
Gets the creation date, source integration, and processing status for a specific dataset.
019d7583get sentiment analytics
Retrieves a summary of positive, neutral, and negative sentiment scores across the entire dataset.
019d7583list analysis datasets
Lists all conversation datasets uploaded for analysis, including record counts and status.
019d7583list available nlp models
Lists the NLP models available for categorizing conversation data (e.g., sentiment, intent, clusterers).
019d7583list connected sources
Lists external platforms (like Zendesk or Intercom) connected to Deep Talk and the volume of data pulled from them.
019d7583list conversation clusters
Lists groups of semantically similar conversations, showing cluster sizes and representative keywords.
019d7583list extracted topics
Lists key themes and topics found in the conversation data, along with their prevalence and importance scores.
019d7583list processing tasks
Lists active data processing and analysis jobs, showing their current completion percentages.
019d7583search topics by keyword
Finds and returns themes within a dataset that match a specific search term.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
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Start with Deep Talk, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Deep Talk lets your AI agent dig into conversation data. You can pull metadata from your Deep Talk account using get_account_details to check usage limits. You'll see all your conversation datasets with list_analysis_datasets, which gives you record counts and status. You can check how far along a specific dataset is with get_dataset_metadata, knowing its creation date, source integration, and processing status.
Want to know what models you can use? list_available_nlp_models shows you which models—like sentiment, intent, or clusterers—are ready to analyze your data. You can also see what external platforms are linked up with list_connected_sources, including the data volume pulled from sources like Zendesk or Intercom. To check on background work, list_processing_tasks shows you active jobs and their current completion percentage.
When you're ready to run the analysis, you can run get_sentiment_analytics to get a summary of positive, neutral, and negative sentiment scores across the whole dataset. To see the big picture of what people are talking about, list_extracted_topics shows you key themes, their prevalence, and importance scores. You can group similar conversations together with list_conversation_clusters, which lists semantically similar groups and their representative keywords.
If you want to drill down, search_topics_by_keyword finds specific themes that match a keyword you give it. Finally, you can find all the topics by calling list_extracted_topics.
How Deep Talk MCP Works
- 1 Connect the Deep Talk integration to your AI client.
- 2 Authorize the connection using your Deep Talk API Key.
- 3 Your AI client runs a tool (e.g.,
get_sentiment_analytics) to pull structured data insights.
The bottom line is, your AI client gets deep insights into customer conversations without needing to write complex database queries.
Who Is Deep Talk MCP For?
This is for CX Managers, Data Analysts, and Product Managers who need to turn raw chat logs and support tickets into actionable data. If you're tired of spending days manually reading tickets to find trends, this is for you. Your agent handles the heavy lifting.
Uses the tool to quickly identify common customer pain points and emerging issues from support channels.
Uses the tool to retrieve structured conversation clusters and sentiment data for deep research via natural chat commands.
Uses the tool to monitor feedback themes from support channels, directly informing the product roadmap and feature prioritization.
What Changes When You Connect
- See Pain Points Instantly: Use
list_extracted_topicsto automatically list key themes and topics, showing their prevalence and importance scores in your customer data. You don't have to read thousands of tickets to know what's wrong. - Gauge Customer Mood:
get_sentiment_analyticsreturns a direct breakdown of positive, neutral, and negative sentiment scores across your entire dataset. This gives you a measurable pulse check on customer satisfaction. - See Source Health:
list_connected_sourceslists every external platform (Zendesk, Intercom) you're pulling data from, confirming the synchronization status and the total volume of data ingested from each. - Find Similar Conversations:
list_conversation_clustersgroups conversations by semantic similarity, showing clusters and representative keywords. This is better than just searching keywords because it finds the meaning behind the text. - Keep Track of Progress: Use
list_processing_tasksto monitor all your data jobs. It shows active ingestion and NLP analysis tasks and how far along they are, so you know when the data is ready. - Know Your Data Scope:
list_analysis_datasetslists all conversation datasets you have uploaded, letting you track metadata and processing status at a glance.
Real-World Use Cases
Finding the Root Cause of Complaints
A Product Manager needs to know if 'login issues' or 'pricing' are causing the most churn. They ask their agent to use list_extracted_topics and get_sentiment_analytics on the 'Q2 Support Data'. The agent returns the top three topics and confirms that negative sentiment is highest around the 'Pricing' cluster, telling the PM exactly where to focus the roadmap.
Auditing Data Sources
A Data Analyst suspects that data from one source, like Intercom, isn't syncing properly. They use list_connected_sources to check the sync status and data volume. If the numbers look off, they can then use list_processing_tasks to check if the necessary analysis job was run.
Investigating a Specific Complaint Trend
A CX Manager hears chatter about a new bug and needs to confirm the impact. They ask the agent to search_topics_by_keyword for 'checkout bug' and then run list_conversation_clusters to see if that specific complaint formed a large, distinct cluster of similar conversations.
Onboarding New Data Streams
A team is preparing a new dataset. They first use list_available_nlp_models to see what analysis types are available. Then, they use list_analysis_datasets to see how many datasets already exist and then kick off the processing using get_dataset_metadata.
The Tradeoffs
Treating Topic Search as a Simple Keyword Match
Searching for 'billing' and only getting results that mention the word 'billing'. This misses conversations about 'subscription charges' or 'invoice discrepancies' that use different language.
→
Use list_extracted_topics or search_topics_by_keyword to find themes. These tools analyze the context, not just the words. You get the underlying topic, regardless of how the customer phrased it.
Manually Correlating Sentiment Scores
Exporting a CSV of 1,000 tickets and manually assigning a sentiment score (positive/negative) to track trends. This is slow, inaccurate, and doesn't account for context shifts.
→
Run get_sentiment_analytics. This tool calculates the distribution of positive, neutral, and negative sentiment across the entire dataset, giving you a reliable, aggregated score instantly.
Assuming All Data is Ready for Analysis
Running get_sentiment_analytics on a dataset that hasn't finished ingesting data from Intercom. The results are incomplete or fail entirely.
→
Always check the status first. Use list_processing_tasks to confirm all ingestion and NLP tasks are marked as 'Completed' before running any analysis tools.
When It Fits, When It Doesn't
Use Deep Talk if your goal is understanding why customers feel the way they do, not just what they said. You need to move beyond simple keyword searches and analyze the underlying meaning and emotional tone of conversations. Use this if you need to find patterns like 'Pricing complaints are highly negative and center on the billing cycle.'
Don't use this if your primary need is simple data storage or basic ETL. If you just need to move data from Point A to Point B without interpretation, look for a basic data pipeline tool. If you only need to check if a single field is populated, use a basic data validation tool instead.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Deep Talk. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sifting through support tickets is a massive time sink.
Right now, finding what customers are complaining about means exporting data from Zendesk, opening a spreadsheet, and manually reading thousands of tickets. You spend hours filtering by keyword, then manually reading the summaries to see if the issue is a 'feature gap' or just a 'misunderstanding.' It’s slow, and you always miss the subtle trends.
With the Deep Talk MCP Server, you just ask your agent to analyze the data. It automatically runs the NLP models, extracting topics and clustering conversations. You get an instant, structured report that highlights the top three pain points and shows exactly how negative the sentiment is around each one. No manual reading required.
Deep Talk MCP Server: Get structured conversation insights.
The manual steps that vanish are exporting data, running separate sentiment checks in a BI tool, and then cross-referencing those numbers with a separate topic model. You have to stitch together multiple data points just to get a single narrative.
Now, you ask for the summary. The agent runs `get_sentiment_analytics` and `list_extracted_topics` in sequence. The system synthesizes both results into one clear answer: 'The primary topic is X, and the sentiment surrounding it is Y.' It's immediate, comprehensive, and built for action.
Common Questions About Deep Talk MCP
How do I check if my dataset is ready for analysis using Deep Talk MCP Server? +
Run list_processing_tasks. This tool lists all current jobs—both ingestion and NLP analysis—and shows their completion percentages. You need to confirm the status is 'Completed' before running analysis tools like get_sentiment_analytics.
What is the difference between `list_extracted_topics` and `search_topics_by_keyword`? +
list_extracted_topics lists all the key themes and topics found in the entire dataset, showing their importance scores. search_topics_by_keyword narrows that down, finding only the themes that match a specific term you provide.
Can I check which external platforms are connected to Deep Talk MCP Server? +
Yes, use list_connected_sources. This tool lists all external platforms (like Intercom or Zendesk) connected to Deep Talk, along with the data volume pulled from each.
How do I check the overall sentiment for a large dataset? +
Use get_sentiment_analytics. This tool returns a summary distribution of positive, neutral, and negative sentiment scores across the entire dataset records.
How many NLP models are available for Deep Talk MCP Server? +
Run list_available_nlp_models. This tool shows every model (sentiment, intent, clusterers) you can apply to your conversations for deeper analysis.
How do I use `get_account_details` to check my Deep Talk processing credits? +
It returns your account's usage limits and subscription tier. You can see exactly how many processing credits you have left and your current user roles.
When should I use `list_processing_tasks` instead of `get_dataset_metadata`? +
list_processing_tasks shows all active jobs, including ingestion and NLP analysis tasks, and their percentage completion. get_dataset_metadata gives static details like creation date and source integration.
Can I use `list_conversation_clusters` to find groups of similar conversations? +
Yes, it returns groups of semantically similar conversations. This list includes cluster sizes and representative keywords, helping you identify patterns.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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