GPTBots MCP. Control your bots, knowledge, and workflows from your agent.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
GPTBots MCP Server manages your entire conversational AI stack. Use it to interact with deployed bots, check conversation histories, and upload new documents directly through your AI client.
You can also trigger complex, automated workflows and query data from the underlying platform database. It's your single point of control for enterprise AI infrastructure.
What your AI agents can do
Create knowledge document
Uploads a file or creates a new document in the Knowledge Base.
Get conversation
Retrieves the details and full chat history for one specific conversation.
List conversations
Lists all recent chat conversations associated with a bot.
List active chats and send direct messages to specific AI agents.
Upload new files or create new documents to feed context into your AI bots.
Trigger complex, multi-step business processes and track their status.
List tables and records in the GPTBots backend database for data analysis.
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GPTBots MCP Server: 8 Tools for AI Management
Manage your entire AI stack—from conversational history to knowledge indexing and workflow execution—using these eight tools.
019d75aacreate knowledge document
Uploads a file or creates a new document in the Knowledge Base.
019d75aaget conversation
Retrieves the details and full chat history for one specific conversation.
019d75aalist conversations
Lists all recent chat conversations associated with a bot.
019d75aalist databases
Lists the available tables within the GPTBots platform database.
019d75aalist knowledge documents
Lists all documents currently stored in the Knowledge Base.
019d75aaquery workflow
Checks the current execution status and record details of a triggered workflow.
019d75aasend bot message
Sends a new message directly to a specified GPTBots AI Agent.
019d75aatrigger workflow
Starts an automated, predefined workflow process.
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 GPTBots, 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
GPTBots MCP Server - Manage AI Knowledge & Bots
This server lets your AI client handle your whole conversational AI setup. You can talk to deployed bots, check chat history, and even upload new documents straight from your client. It's your one spot to manage the whole enterprise AI deal.
Managing Bot Conversations
You can check out all your recent chats with a bot using list_conversations, grab the full chat history for one specific conversation with get_conversation, and send new messages directly to any AI Agent using send_bot_message.
Updating Knowledge Bases
Need to feed your bots new context? You can upload a file or make a new document in the Knowledge Base with create_knowledge_document. You can also see what documents are already stored in the Knowledge Base by running list_knowledge_documents.
Running Automated Workflows
Start a complex, multi-step business process by calling trigger_workflow, and you can track exactly what's going down with query_workflow to check the status and record details of any run.
Auditing Platform Data
Want to dig into the backend data? You can list all the available tables in the GPTBots platform database using list_databases, and you can also check out what's in there.
How GPTBots MCP Works
- 1 Subscribe to the GPTBots server and input your API Key and Data Center Region.
- 2 Your AI client sends a request (e.g., 'List all conversations for bot X').
- 3 The server executes the required tool call and returns the data or confirmation to your client.
The bottom line is, you control your entire AI stack using natural language commands, eliminating the need to switch between a web console and your code editor.
Who Is GPTBots MCP For?
This is for developers and operations staff who live in their IDE. If you spend time toggling between a web UI, a terminal, and your code editor to manage AI bots, this saves you time. It lets you treat your AI system like a library you can query and modify directly from your agent.
Tests bot responses, updates the knowledge base, and triggers workflows directly from the IDE without leaving the code editor.
Audits conversation histories to evaluate how well the bots perform and whether users are satisfied with the output.
Integrates GPTBots automated workflows into existing daily processes by calling them programmatically from scripts or agents.
What Changes When You Connect
- Manage your entire bot conversation history. Use
list_conversationsandget_conversationto audit chat logs and understand bot performance. - Keep your bots current with
create_knowledge_document. Uploading new files immediately updates the context for all your agents. - Automate processes with
trigger_workflow. You can initiate complex, multi-step tasks and then usequery_workflowto track the results. - Audit the platform data. Use
list_databasesto see what tables are available, then uselist_knowledge_documentsto see what data is indexed. - Streamline development. Instead of switching tabs, you can send a test message using
send_bot_messageand get an immediate response, all through your agent. - Consolidate management. You handle bot interactions, knowledge updates, and workflow execution using a single, consistent interface.
Real-World Use Cases
Evaluating Bot Performance
A Product Manager needs to check if the bot handled a complex query correctly. They use list_conversations to find the ID, then get_conversation to pull the full transcript. They can then review the chat history to assess user satisfaction without logging into the web portal.
Updating Bot Context
The Ops team receives a new compliance manual. Instead of manually uploading it through the web dashboard, they use create_knowledge_document to inject the new PDF directly into the Knowledge Base, ensuring the bot knows the latest rules immediately.
Running a Full Onboarding Sequence
A developer needs to test a new onboarding process. They use trigger_workflow to start the sequence, passing necessary parameters. They then use query_workflow repeatedly until the status confirms the process completed successfully.
Debugging Bot Issues
The bot gives a weird answer. The developer first uses send_bot_message to prompt a specific query, then checks the chat history using get_conversation to see the immediate context and debug the issue.
The Tradeoffs
Manual Dashboard Crawling
Trying to update bot context by manually navigating the GPTBots web dashboard, uploading files, and then checking the activity log in a separate tab. This takes minutes and is error-prone.
→
Use create_knowledge_document to upload files and list_knowledge_documents to confirm they are indexed. Use your agent to wrap these calls for a single command.
Sequential Tool Blindness
Calling trigger_workflow and then forgetting to check the status. The workflow might fail silently, and the user never knows why the process stalled.
→
Always call query_workflow immediately after trigger_workflow to get the record ID, and then check the status until it completes.
Chatting without Context
Asking a bot a question and then having to switch to a different tool just to see the chat history. The context gets lost in the manual switching process.
→
Use list_conversations to find the correct chat ID, and then use get_conversation to pull the full history right next to your other debugging tools.
When It Fits, When It Doesn't
Use this server if your primary need is centralized control over an existing, complex AI ecosystem. You need to treat your bots, knowledge base, and automated processes as services you can query and modify programmatically. You must use this if you need to audit conversation history or trigger workflows from your IDE.
Don't use this if you just need a simple, one-off chatbot interface (a basic messaging tool). For that, a simple API key and a single send_bot_message call is enough. You'll ignore the rest of the platform's capabilities (like list_databases or query_workflow) and miss out on the full value of the platform.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by GPTBots. 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 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Managing AI agents usually means bouncing between five different tabs.
Today, managing your AI bots means logging into the GPTBots web portal, finding the chat ID, checking the knowledge documents in one tab, and running the workflow in a separate section. If you want to test a bot's response, you have to switch out of your code editor, copy the conversation ID, and paste it into a separate logging screen.
With the GPTBots MCP Server, you manage it all from your AI client. You use `list_conversations` to find the ID, then `get_conversation` to pull the history, and you can even `create_knowledge_document` to update the context—all in one seamless flow. You just get the answers without leaving your dev environment.
GPTBots MCP Server: Trigger and Track Automated Workflows
Manually running a complex workflow requires clicking 'Start' on the web dashboard, waiting, and then having to switch to a status screen to see if it succeeded or failed. If it fails, you have to guess why and repeat the process.
Now, you use your agent to `trigger_workflow` with the necessary parameters. The agent gets a Record ID back, and you immediately use `query_workflow` to check the status. You get predictable, actionable status updates, every time.
Common Questions About GPTBots MCP
How do I use the `list_conversations` tool? +
The list_conversations tool lists all active chats for a bot. You need to pass the bot's identifier to the tool call, and the result will give you a list of conversation IDs and their last updated times.
What is the best way to update knowledge with `create_knowledge_document`? +
To update knowledge, use create_knowledge_document and provide the document file or content. The system will then index it and make the information available to the bot's context.
Can I see the full chat history using `get_conversation`? +
Yes, get_conversation retrieves the complete message exchange for a specific conversation ID. This is useful for auditing bot performance and understanding the full context of a query.
How do I check the status of a workflow using `query_workflow`? +
Call query_workflow with the execution Record ID. It returns the current status (running, completed, failed) and any associated error messages for debugging.
Do I need to call `send_bot_message` every time I want to chat? +
No. While send_bot_message sends a message, list_conversations and get_conversation let you view past chats. You use send_bot_message when you actively want to send a new message.
What happens if I use `list_databases` and want to check a specific table? +
The tool lists all available tables in the platform database. You then need to use the table name to write a specific query or function call to read the records you want.
Can I combine `trigger_workflow` with parameters like `email` or `user_id`? +
Yes, you pass parameters directly when calling trigger_workflow. This allows you to customize the workflow's input data, such as specifying a user's email or a record ID.
Does `list_knowledge_documents` show the document content, or just the titles? +
It only lists the documents available in the Knowledge Base. If you need the content, you must use a separate tool or function call to retrieve the full text of the document.
How do I chat with a specific bot? +
Use the send_bot_message tool and provide the Bot ID and your message content. The AI agent will relay your message to the GPTBots platform and return the bot's response.
Can I check the status of a triggered workflow? +
Yes. When you use trigger_workflow, it returns a Record ID. You can then pass that Record ID to the query_workflow tool to monitor its execution status (e.g., Running, Success, Failed).
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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