Observe.AI MCP. Analyze full transcripts and QA scores with natural conversation.
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Observe.AI connects your AI agent directly to your contact center performance data. You can list every call and chat interaction, pull full transcripts instantly, check specific QA scores, and get automated summaries—all without leaving your chat interface.
What your AI agents can do
Get evaluation details
Retrieves a specific quality assurance score and detailed evaluation report for an interaction.
Get interaction details
Gets core metadata about a particular call, chat, or email interaction.
Get interaction transcript
Pulls the full conversation transcript for any specific interaction ID.
The agent retrieves metadata for every call, chat, or email processed by the platform.
The agent pulls the complete text of any specified interaction's dialogue.
The agent fetches specific quality assurance scores and detailed evaluation forms for an interaction ID.
The agent pulls automated, high-level summaries of interactions, identifying key business themes or moments.
The agent tracks and lists past coaching sessions and associated feedback logs for an agent.
The agent retrieves a list of all agents, supervisors, and administrators within the Observe.AI instance.
Ask AI about this MCP
Supported MCP Clients
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Observe.AI MCP Server: 10 Tools for CX Analysis
Execute interaction analysis, transcript retrieval, evaluation score fetching, and user list management using these ten tools via your AI client.
019d75e1get evaluation details
Retrieves a specific quality assurance score and detailed evaluation report for an interaction.
019d75e1get interaction details
Gets core metadata about a particular call, chat, or email interaction.
019d75e1get interaction transcript
Pulls the full conversation transcript for any specific interaction ID.
019d75e1list coaching sessions
Lists all coaching sessions recorded for a specified agent, showing dates and topics.
019d75e1list evaluation forms
Retrieves a list of available QA evaluation forms used in the system.
019d75e1list interaction moments
Lists key business moments (like greetings or objections) identified across multiple interactions.
019d75e1list interaction summaries
Pulls AI-generated, high-level summaries of recent customer service interactions.
019d75e1list interactions
Lists all contact center interactions that have been processed by the platform.
019d75e1list qa evaluations
Retrieves a list of all completed quality assurance evaluations and their basic scores.
019d75e1list workspace users
Lists the agents, supervisors, and administrators set up within your Observe.AI workspace.
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
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Observe.AI, 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
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Observe.AI connects your AI agent directly to your contact center performance data. You'll use it to analyze conversations across calls, chats, and emails—all without leaving your chat interface.
Listing Interactions & Context Discovery
You can start by getting a comprehensive view of everything that happened using list_interactions, which pulls metadata for every call, chat, or email processed through the platform. If you need to know who's in the system, list_workspace_users gives you a roster of all agents, supervisors, and administrators set up in your Observe.AI workspace.
For deep dives into specific conversations, the agent pulls full context using get_interaction_details, which retrieves core metadata about any given interaction ID. You can pull the complete text conversation for any specific exchange with get_interaction_transcript. When you need a quick grasp of what was discussed, list_interaction_summaries generates AI-written, high-level summaries of recent customer service interactions.
Beyond general summaries, list_interaction_moments identifies key business themes—things like greetings or objections—that were flagged across multiple interactions.
Quality Assurance and Performance Tracking
The server lets you validate agent performance using several quality tools. You can get a full list of all completed evaluation reports via list_qa_evaluations, which provides basic scores for every QA check done. To see exactly what metrics were used, the agent calls list_evaluation_forms to retrieve a catalog of available QA evaluation forms in your system.
When you need granular detail on one specific interaction's performance, get_evaluation_details fetches that particular quality assurance score and the full detailed report associated with it.
Coaching History and Agent Management
You can track an agent’s development by listing their coaching history. The tool list_coaching_sessions pulls every recorded coaching session for a specified agent, detailing when those sessions happened and what topics were covered during the review. This gives your AI client a full timeline of feedback logs. By chaining these calls—listing interactions, getting the transcript, then checking the QA scores—your agent builds a complete performance dossier on demand.
How Observe.AI MCP Works
- 1 First, subscribe to this server and provide your Observe.AI API Key (Bearer Token).
- 2 Next, prompt your AI client with a natural language request—for example: 'What was the QA score for interaction X?'
- 3 The agent executes the necessary tool (like
get_evaluation_details) and returns the specific data point or list you requested.
The bottom line is, you talk to your AI client like talking to a coworker who already has access to all of your contact center reports.
Who Is Observe.AI MCP For?
This is for the QA Analyst stuck clicking through dozens of dashboards just to find one score. It's for Managers running daily standups who need high-level performance trends instantly, and Coaches who spend too much time manually tracking agent improvement.
They use the agent to check specific evaluation scores or pull full transcripts for random audits without having to open the main portal.
They monitor high-level performance trends and ask for AI summaries during daily standups, quickly identifying recurring issues like 'Portal Login' problems.
They verify coaching session history or check an agent’s progress by asking the agent to list past feedback logs and improvement metrics.
What Changes When You Connect
- Stop opening multiple dashboards. Use
list_interactionsto see a quick list of recent calls, then useget_interaction_transcriptto pull the full text for review in one go. - Get instant performance checks without logging into QA tools. Simply ask your agent, and it uses
get_evaluation_detailsto return specific scores (e.g., 'Empathy: 9/10'). - Track team improvement history directly from the chat. Call
list_coaching_sessionsto list an agent’s past feedback logs and verify progress instantly. - Cut through noise with AI summaries. Use
list_interaction_summariesto see immediate thematic insights, like 'Renewals' or 'Billing Inquiries,' across dozens of calls. - Know who you're working with. Run
list_workspace_usersto list all roles and accounts in your instance without navigating the user management panel. - See patterns over time. By checking
list_interaction_moments, you can quickly see if 'Objections' are rising or falling across different product lines.
Real-World Use Cases
Investigating a sudden dip in QA scores.
A manager notices the average compliance score dropped last week. Instead of manually checking every report, they ask the agent to run list_qa_evaluations and then use get_interaction_details on the worst-scoring interactions. The agent filters these down and highlights a consistent failure point—a missing mandatory disclosure script.
Training a new agent on compliance.
A supervisor wants to show a trainee exactly how to handle an objection. They ask the agent to run get_interaction_transcript for a successful 'Objection' call, letting the trainee read the real-life dialogue and understand the required phrasing.
Identifying systemic product issues.
A QA analyst wants to know if customers are struggling with one feature. They ask the agent to run list_interaction_moments and filter for 'Objection.' The resulting list shows that 70% of objections relate to 'Portal Login' difficulty, immediately pinpointing a product gap.
Preparing for a team meeting.
A manager needs the highlights from yesterday’s calls. They ask the agent to run list_interaction_summaries and narrow it down by theme. The resulting summary tells them, 'Billing Inquiries' is the dominant topic, giving them the perfect agenda point for their standup.
The Tradeoffs
Clicking through dashboards.
Opening the Observe.AI portal, navigating to 'Analytics,' then filtering by date range, and finally clicking on 15 different interaction IDs just to copy transcripts for a meeting.
→
Trying to summarize manually.
Reading an entire chat transcript (which can be thousands of words) line-by-line in preparation for a coaching session, which takes half a day and is prone to human error.
→
Forgetting the full context.
Finding a good score via get_evaluation_details but not knowing why it was high. The user just sees '95%' and doesn't know if it was for speed or empathy.
→
Overloading the prompt.
Asking the agent to 'list all interactions, summarize them, check QA scores, and list coaching sessions' in one massive block of text. The agent might fail due to scope creep.
→
When It Fits, When It Doesn't
Use this server if your primary workflow involves deep data retrieval across multiple domains (QA scoring, transcripts, summaries) and you need those results returned instantly into a conversational format. It's perfect for the QA Analyst or Supervisor who needs verifiable facts now.
Don't use it if all you need is general reporting—like viewing aggregated metrics that are already displayed on a single dashboard view. If your goal is just to look at the raw, unparsed data feeds (e.g., streaming analytics), you might be better off connecting an MCP server designed for real-time web hooks or database polling tools instead of relying on structured historical record fetching.
If you need to see what people can do with this tool, start by listing the interactions using list_interactions. This is the entry point; everything else flows from there.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Observe.AI. 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
Finding key details in customer service records shouldn't feel like detective work.
Today, checking a single agent’s performance means logging into three different systems: the main interaction log to find the call ID; the QA dashboard to pull the score; and then opening a separate document viewer just for the transcript. You spend half your time copying IDs and clicking through tabs instead of analyzing the data.
With this MCP server, you simply talk to your agent. If you need to know why a call failed, you ask it directly. The agent executes `get_evaluation_details` and immediately returns the score alongside the full text from `get_interaction_transcript`. You get the answer, not five links.
Observe.AI MCP Server: Get transcripts and summaries instantly.
The manual steps that evaporate are the cross-system lookups. No more copy/pasting interaction IDs between your CRM, your QA system, and a spreadsheet. You don't need to switch contexts; you just ask for what you need.
This changes everything. Your agent gives you synthesized answers—it doesn't just give you raw data points. It lets you run `list_interaction_summaries` and get the 'what happened' narrative right in your chat window.
Common Questions About Observe.AI MCP
How do I list all my recent calls using list_interactions? +
You ask the agent to run list_interactions. It returns a list of call IDs, durations, and basic metadata. You can then select specific IDs from that list for deeper analysis.
Can I get a transcript using get_interaction_transcript? +
Yes. If you provide the agent with a valid interaction ID, it runs get_interaction_transcript and returns the complete dialogue text for review.
What is list_qa_evaluations used for? +
list_qa_evaluations gives you a clean roster of all completed QA reviews. It's the starting point if you need to check historical performance data or identify an average score.
How does list_interaction_moments help me coach? +
list_interaction_moments identifies specific conversational beats, like 'Greeting' or 'Objection,' across multiple calls. This helps you teach agents what those moments sound like in real life.
Does list_coaching_sessions show my progress? +
Yes, list_coaching_sessions retrieves the history of coaching sessions for a specific agent. You can use this to track if they are engaging with feedback over time.
When I run `get_evaluation_details`, what specific data points do I get for an agent's performance? +
The tool returns granular metrics including overall QA scores, individual criterion grades (e.g., Empathy, Compliance), and the total evaluation date. This structured output lets you programmatically filter agents who fell below a certain score threshold or need coaching on specific criteria.
How can I use `list_workspace_users` to check my team's roles and access levels? +
It lists all users—admins, supervisors, and agents—within your Observe.AI workspace. This function confirms who has permission to view sensitive data or modify settings, helping you manage access control without navigating the main portal.
What kind of business themes does `list_interaction_summaries` identify? +
The tool provides AI-generated summaries that extract key discussion topics and recurring customer issues (like 'Billing Inquiries' or 'Portal Login'). Instead of reading transcripts, you immediately see the high-level operational theme for a batch of calls.
How do I get an Observe.AI API Key? +
You can find your API Key in the Observe.AI dashboard under Settings > API Keys. This token is used as a Bearer Token for authentication.
Can I read full call transcripts through the agent? +
Yes! Use the get_interaction_transcript tool with a specific Interaction ID to retrieve the full text content of the conversation.
What information is included in the QA evaluations? +
Evaluations include the QA form used, individual section scores, overall score, and specific feedback or comments provided by the evaluator.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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