SigmaMind AI MCP for AI. Run, track, and analyze voice calls programmatically.
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SigmaMind AI connects voice workflows to your agent, letting you manage calls and analyze conversations through your preferred AI client.
You can initiate a call using `create_call` or list all existing ones with `list_calls`. The server exposes tools for full transcript retrieval (`get_call_transcript`), sentiment analysis (`get_call_analysis`), and configuring dedicated voice agents via `create_agent`.
It's built for automating communication processes without writing complex backend code.
What your AI can do
Check sigmamind status
Verifies if the API connection to SigmaMind is active and functioning correctly.
Create agent
Creates a new, custom-prompted AI voice agent profile for specific communication tasks.
Create call
Initiates a live voice call to a specified number using a designated agent.
List existing agents with list_agents, or create new voice personalities using create_agent.
Initiate a live, recorded conversation via create_call, and retrieve the status of any call through get_call.
Pull full text transcripts from completed interactions using get_call_transcript.
Run get_call_analysis to receive machine-generated data on sentiment, key topics, and performance metrics for a call ID.
Check the API connection health using check_sigmamind_status before running any workflow.
Ask an AI about this
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SigmaMind AI: 10 Tools for Communication Automation
These tools let your agent manage the entire lifecycle of a phone call—from setup to analysis—all through direct tool invocation.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using SigmaMind AI on VinkiusCheck Sigmamind Status
Verifies if the API connection to SigmaMind is active and functioning correctly.
Create Agent
Creates a new, custom-prompted AI voice agent profile for specific communication...
Create Call
Initiates a live voice call to a specified number using a designated agent.
Get Agent
Retrieves detailed information about a specific AI agent by its ID.
Get Call Analysis
Generates structured insights, including sentiment scores and topic breakdowns, from...
Get Call
Gets basic details and metadata for a completed or ongoing call session.
Get Call Transcript
Retrieves the full, time-stamped text transcript of any completed voice call.
List Agents
Lists all existing AI agents configured in your SigmaMind account.
List Calls
Retrieves a list of recent calls, providing IDs and status for easy tracking.
List Numbers
Displays all associated phone numbers available for calling campaigns.
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The SigmaMind AI integration is available immediately — no restart needed.
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.
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Start with SigmaMind AI, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
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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 connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Tracking Call Outcomes Shouldn't Require Manual Data Merging
Right now, if you run a campaign, you get call logs in one place, recordings in another, and then you have to export the transcripts manually. You spend hours jumping between systems—downloading audio files, transcribing them with third-party tools, and finally running sentiment analysis on a spreadsheet.
With this MCP server, that whole process collapses. Your agent handles it all. Run `create_call`, let it happen, then ask for the results via `get_call_analysis`. You get structured data—sentiment scores, key topics—pushed directly back to your client, no spreadsheets needed.
SigmaMind AI MCP Server: Get Call Transcripts with `get_call_transcript`
Before this, pulling a conversation record meant dealing with proprietary file formats or relying on imperfect web hooks. You'd often only get the summary—a snippet of what was said—and had to manually trigger another process just for the full text.
Now, calling `get_call_transcript` gives you the complete, time-stamped record immediately. It’s raw data at your fingertips; no more partial summaries.
What your AI can actually do with this
Listen up. This SigmaMind AI MCP Server connects your agent directly to a whole stack of voice communication tools through your AI client. You don't gotta write complex backend code; you just point your agent at these functions and it handles the heavy lifting.
You need to know what numbers you got? Use list_numbers to display every phone number available for calling campaigns. Before you run anything, check the connection health first. Running check_sigmamind_status verifies if the API link to SigmaMind is live and working right.
When it's time to build out your communication stack, you can start by checking what AI agents are already set up with list_agents. If you need a new personality for specific tasks, you use create_agent to generate a custom-prompted voice agent profile. Once that’s done, if you want details on the agent you just made or one from before, run get_agent by its ID to pull up all the info.
Ready to make a call? You initiate a live conversation using create_call, specifying both the target number and the designated voice agent. When that call wraps up, you can grab basic metadata for the session—whether it’s still going or finished—by calling get_call.
But getting the basics isn't enough. You gotta understand what was said. Use get_call_transcript to retrieve the full text transcript of any completed voice call, complete with time stamps so you know exactly when things were mentioned. For deep analysis, running get_call_analysis gives you structured insights: it pulls out sentiment scores and breaks down key topics from that recorded conversation based on a specific call ID.
To keep track of the calls you run—finished or still ongoing—you can pull up a list of recent sessions using list_calls, which provides IDs and status for quick tracking. If you've got an agent ID, running get_agent lets you retrieve all the detailed information about that specific voice personality.
Basically, your workflow runs like this: You check the system health with check_sigmamind_status. Then, you manage who’s talking by using list_agents and setting up a custom persona with create_agent. When you're ready to go out, you trigger it all with create_call, pointing it at a number retrieved from list_numbers. Once the hang-up happens, your agent can pull the full text using get_call_transcript and generate concrete performance metrics—sentiment scores, key topics, etc.—by executing get_call_analysis on that call ID.
You can also check basic details of any session with get_call, or look up specific agent info again with get_agent. It's a full loop for managing and analyzing every single voice conversation.
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Here's how it actually works
The bottom line is that your AI client manages the entire voice lifecycle—from setup through execution and analysis—without you needing to interact with a web dashboard.
First, use list_agents to confirm existing agent profiles. If you need a new one, call create_agent, providing the necessary prompt context.
Next, call create_call, passing the target phone number and the specific agent ID. This starts the live conversation flow.
Finally, once the call concludes, execute get_call_analysis with the resulting call ID to pull structured sentiment scores and topic breakdowns.
Who is this actually for?
This server is for communication ops teams, sales enablement managers, or business analysts who are sick of manually compiling call reports. If your job involves running high-volume outbound campaigns or qualifying leads by phone, this saves you hours of tedious data aggregation and report building.
Runs structured qualification calls using a dedicated agent created via create_agent, then uses get_call_analysis to instantly score the lead's interest level.
Needs to track call volumes and service quality. They use list_calls and list_agents to audit performance across all campaigns, verifying connectivity with check_sigmamind_status first.
Manages automated support calls. After a call completes, they grab the transcript via get_call_transcript to quickly identify common pain points and update knowledge bases.
What Changes When You Connect
Analyze Outcomes Instantly: Stop reviewing PDFs. Use get_call_analysis to get instant, structured data on sentiment (positive/negative) and key topics from any call ID. This turns raw audio into quantifiable metrics.
Manage Agents via Code: Don't mess with web dashboards. Your agent uses list_agents and create_agent to deploy multiple voice personas—one for sales, one for support, etc.—all controlled by simple prompts.
Full Audit Trail on Demand: Need to see exactly what was said? Call get_call_transcript. It pulls the complete text log of a call, making it trivial to search for specific keywords or quotes later.
Automate Campaign Execution: Start calls with one command. Calling create_call lets your agent handle complex conversations (like qualifying leads) without any manual intervention from you during the process.
Centralized Call Oversight: Get a birds-eye view of everything using list_calls. You track call history, check status via get_call, and maintain accountability across large teams.
See it in action
Lead Qualification Audit
A sales rep needs to audit a week's worth of calls. Instead of downloading 20 separate recordings, they ask their agent: 'Get the analysis for all my calls last Tuesday.' The agent runs list_calls, then loops through IDs, calling get_call_analysis on each one. They end up with a single JSON object containing sentiment scores and topic breakdowns for every call.
Support Bot Deployment
The support team is rolling out a new automated bot. Before deployment, they use list_agents to check the current setup. They then run create_agent, uploading a detailed script and prompt. Finally, they test it by calling create_call with a dummy number, validating that the agent performs as expected.
Debugging Call Flow
A call fails midway through. Instead of guessing what went wrong, an engineer uses get_call to check the basic metadata for the failing ID. If the data looks right, they run get_call_transcript and immediately see where the conversation dropped or deviated from the expected script.
Multi-Campaign Reporting
A manager runs three different campaigns (Sales, Survey, Support). They use list_numbers to verify all associated lines. Then they ask the agent to summarize all call data using a sequence of calls: create_call, followed by gathering transcripts via get_call_transcript for each run.
The honest tradeoffs
Trying to pull all data at once
A user tries to get the 'full report' by simply calling one vague function, assuming it knows which call ID they mean or what kind of analysis they want.
You have to be specific. First, use list_calls to identify the exact Call ID you need. Then, run get_call_analysis (for scores) OR get_call_transcript (for text). Don't mix those requests.
Assuming agents are configured
The user tries to initiate a call using create_call without knowing if the correct voice agent exists or what its prompt is.
Always check first. Run list_agents to see available profiles. If you need one, use create_agent before calling create_call. The process fails early if the agent doesn't exist.
Ignoring system health
Running a complex workflow like creating an agent or initiating calls immediately after a deployment without checking connectivity.
Start with check_sigmamind_status. It verifies the API connection. If that tool returns a success code, then you know your next command, like create_agent, will actually execute.
When It Fits, When It Doesn't
Use this server if your workflow requires automating communication actions—calling numbers and analyzing conversation text. Specifically, if you need to track sentiment or get full transcripts after an automated call, this is the tool. Don't use it if you just need to manage simple records; for instance, if you only needed a list of contact emails (not phone calls), that would be better handled by a dedicated CRM API. Use list_numbers when you only care about available lines, but remember that running an actual call requires create_call and the necessary agent setup using create_agent. If your goal is simply data storage without any communication component, this server won't help.
Questions you might have
How do I find out if I can run a call with SigmaMind AI using `create_call`? +
You must first check the status and availability. Run check_sigmamind_status to confirm API connectivity, then use list_numbers to verify an active number is assigned before calling create_call.
What data do I need for `get_call_analysis`? +
You only need the call ID. The tool runs the analysis on the recorded session associated with that ID, returning sentiment and topic breakdowns.
Can I reuse an agent created with `create_agent` for a different campaign? +
Yes. Once you run get_agent, you'll see its details and prompts; those configurations can be reused or slightly modified to handle new campaigns via subsequent calls.
What is the difference between `list_calls` and `get_call`? +
list_calls gives you a list of call IDs and basic status flags. get_call takes one specific ID and retrieves detailed metadata about that single, specified call.
How does `check_sigmamind_status` verify my API connectivity? +
It confirms your API connection is active and ready for use. The tool returns a success code or details the specific error, letting you know immediately if there's an issue with authentication or credentials before you start running calls.
What happens if I call `create_call` but the conversation fails? +
The system records a detailed failure status that you can check using get_call. This lets you retrieve error codes and know exactly why the call dropped or couldn't connect, saving you time on troubleshooting.
Are there any usage limits or rate limits for calling tools like `create_call`? +
SigmaMind enforces standard API rate limiting to ensure system stability. If you hit a limit, the tool will return a 429 error code, telling your AI client exactly when you can try again.
How do I handle large amounts of data from `get_call_transcript`? +
The API handles transcripts up to a specified maximum length. For extremely long calls, the tool provides pagination metadata, allowing your agent to fetch the transcript in manageable chunks rather than one massive block of text.
Can my AI initiate phone calls? +
Yes. Use create_call with an agent ID and phone number. The AI agent handles the conversation automatically.
Can I read call transcripts? +
Yes. get_call_transcript returns the full conversation for any completed call.
How do I create a new voice agent? +
Use create_agent with a name and system prompt. The agent will use the prompt to guide conversations.
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