VectorShift MCP for AI. Control your entire AI data lifecycle.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
VectorShift provides full control over complex AI automation and Retrieval-Augmented Generation (RAG) workflows. Use this MCP to manage entire data pipelines, query internal knowledge bases with semantic search, or deploy and interact with custom chatbots—all directly from your agent's conversation.
What AI agents can do with VectorShift (AI Workflow & RAG Automation) Automation
Bulk run pipeline
Runs multiple instances of a defined workflow simultaneously for high-volume processing.
Create chatbot
Initializes and provisions a new, dedicated chatbot instance.
Create knowledge base
Sets up the container for storing indexed organizational knowledge.
Create, run, and control complex, multi-step data pipelines that execute custom logic.
Index documents from files or URLs to create searchable knowledge bases for grounded AI responses.
Deploy, manage, and send messages to specialized chatbots directly through your agent.
Run custom data transformations using Python or JavaScript logic as part of a larger process.
List and control running workflows, allowing you to pause, resume, or terminate instances.
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What AI agents can do with VectorShift (AI Workflow & RAG Automation) MCP - 29 Tools
Use these tools to manage every aspect of your AI application: build workflows, index data, create bots, and execute complex operations programmatically.
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 VectorShift (AI Workflow & RAG Automation) on VinkiusBulk Run Pipeline
Runs multiple instances of a defined workflow simultaneously for high-volume processing.
Create Chatbot
Initializes and provisions a new, dedicated chatbot instance.
Create Knowledge Base
Sets up the container for storing indexed organizational knowledge.
Create Pipeline
Defines and builds a new multi-stage, automated data workflow.
Create Transformation
Builds reusable logic blocks to clean or reshape structured data (Python/JS).
Delete Chatbot
Removes an existing chatbot instance entirely from the system.
Delete Knowledge Base Documents
Removes specific documents from a knowledge base using their unique IDs.
Delete Knowledge Base
Permanently removes a knowledge base container and its associated data.
Delete Pipeline
Deletes an entire workflow pipeline definition by its ID.
Delete Transformation
Removes a custom data transformation logic block.
Get Chatbot
Retrieves the details of a chatbot using either its ID or name.
Get Knowledge Base
Fetches the metadata and status of a knowledge base by ID or name.
Get Pipeline
Retrieves the full definition and configuration of a specific pipeline workflow.
Get Transformation
Gets the current details and code for a defined data transformation logic.
Index Knowledge Base
Adds files, URLs, or documents to be processed and stored within a knowledge base.
List Chatbots
Returns a list of all chatbot instances currently available for use.
List Knowledge Base Documents
Finds and lists the specific documents stored within a knowledge base container.
List Knowledge Bases
Returns a list of all configured knowledge bases in your account.
List Pipelines
Lists every defined workflow pipeline that you have set up.
List Transformations
Retrieves a list of all custom data transformation scripts available.
Pause Pipeline
Stops a currently executing pipeline workflow instance temporarily.
Query Knowledge Base
Performs a semantic search against the knowledge base to find relevant context for...
Resume Pipeline
Restarts one or more pipeline instances that were previously paused.
Run Chatbot
Sends a specific message to a chatbot and waits for the generated response.
Run Pipeline
Executes an entire pipeline workflow with specified inputs, starting its process.
Run Transformation
Runs a saved data transformation script using specific input variables.
Terminate Chatbot
Immediately ends an active chatbot session that is currently running.
Terminate Pipeline
Abruptly stops a running pipeline workflow instance when it's no longer needed.
Upload Chatbot Files
Upload files to a chatbot session
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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 VectorShift 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.
- 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 VectorShift (AI Workflow & RAG Automation), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by VectorShift. 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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Built on the Model Context Protocol (MCP) for 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 29 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Dealing with fragmented knowledge bases is always a mess., Solved with Vinkius AI Gateway
Today, getting answers from internal documents means bouncing between three separate systems: the document repository, the search portal, and the LLM playground. You copy a snippet of text, paste it into your agent's prompt, and hope it’s the right answer because you didn't actually check if that data was current or complete.
With this MCP, the process is contained. You define all source materials—files, URLs, etc.—and push them to a knowledge base using `index_knowledge_base`. Then, when your agent needs context, it calls `query_knowledge_base` directly on that indexed container. The answer you get is guaranteed to come from the data sources you controlled.
Running complex workflows with VectorShift (AI Workflow & RAG Automation)
Before this, a process like 'fetch data -> clean it -> run a query' required three separate API calls or three different team members. You had to manually check the output of one stage before feeding it into the next.
Now, you define that entire chain in one pipeline using `create_pipeline`. You give your agent the initial trigger, and the workflow executes every step—from data transformation (`run_transformation`) to final query (`run_pipeline`)—automatically. It just works.
What your AI can actually do with this
Need to run complicated LLM processes? This connector lets you treat AI automation like any other service: manage it via natural language commands. You can build complex, multi-step workflows that execute data transformations before querying a knowledge base or starting a chatbot session. For example, your agent can first query the available chatbots using list_chatbots, then upload context files with upload_chatbot_files and run the conversation with run_chatbot.
If you need to make sure those processes are reliable across different services—say, linking this automation layer to a billing MCP or a messaging service—Vinkius lets your agent chain them together using one connection. This means you build massive automations without worrying about platform boundaries. You're running everything from the initial setup (create_pipeline) through monitoring (like pausing a workflow with pause_pipeline or terminating it via terminate_pipeline).
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Here's how it actually works
The bottom line is that it gives your AI client the full operational controls—the equivalent of an admin dashboard—to manage all its data sources and workflows.
Subscribe to the MCP and provide your VectorShift API key.
Your agent uses commands (like 'list all pipelines') to see what resources you have created.
You tell your agent which action to take, like running a query against a specific knowledge base ID.
Who is this actually for?
This MCP is for the platform engineer or product architect who has moved beyond simple, single-prompt LLM calls. You need to build reliable, multi-stage applications that process raw data into usable context.
They use this MCP to programmatically test RAG pipelines and index knowledge bases straight from their development environment.
They automate repetitive data processing tasks, such as running bulk jobs or managing the lifecycle of multiple chatbots.
They use it to test how quickly their internal knowledge base can answer complex product documentation queries via an automated agent.
What Changes When You Connect
Need to ground an agent in proprietary information? You can index_knowledge_base using files or URLs, and then use that context immediately when running a query with query_knowledge_base.
Stop relying on manual data prep. By defining custom logic using create_transformation, you ensure the input for any workflow is always clean before execution.
Don't just run a chatbot; manage its entire lifecycle. Use list_chatbots to see what’s available and get_chatbot to confirm its setup.
Handle large-scale jobs without constant babysitting. You can define a pipeline with create_pipeline and then use bulk_run_pipeline to fire off dozens of tasks at once.
When things go sideways, you have control. If a workflow is taking too long, you can pause_pipeline, inspect it, and restart later using resume_pipeline.
See it in action
The Product Team Needs Technical Answers
A product manager needs to know the specific rules for a new feature. Instead of digging through shared drives, they ask their agent to query_knowledge_base using the 'Product Wiki' knowledge base. The agent returns precise text snippets found via semantic search.
The Data Team Needs Batch Processing
A data analyst has 50 spreadsheets that need standardization before being loaded into a new system. They define a transformation with create_transformation and then use the agent to execute it via run_transformation on all files, handling the batch process.
The Support Team Needs an Internal Bot
Support needs instant access to HR policies. They first create a knowledge base with HR docs using index_knowledge_base, then use the agent to deploy and test it by calling create_chatbot and running the conversation through run_chatbot.
The DevOps Team Needs Full Visibility
A developer needs to test a complex data flow. They first define a pipeline using create_pipeline, then run it with initial inputs via run_pipeline. If the job fails, they can immediately check if the instance is running using get_pipeline.
The honest tradeoffs
Treating RAG as a single API call
Just sending text and hoping for context. This misses structured data or multi-step retrieval.
You must first index_knowledge_base with your documents, then use the agent to execute a specific query_knowledge_base command against that container.
Running complex logic manually
Having to copy-paste data through three different scripts just to clean it up.
Group the cleaning steps into one definition using create_transformation, then execute the whole sequence by calling a pipeline via run_pipeline.
Ignoring workflow state
A job starts, runs for hours, and you forget it. You can't interact with it until it finishes.
Monitor the flow by listing available pipelines (list_pipelines) or checking the status using get_pipeline. If it stalls, use pause_pipeline.
When It Fits, When It Doesn't
Use this MCP if your automation requires sequential steps: first ingest data (using index_knowledge_base), then process that data (via a defined pipeline), and finally query the result (with query_knowledge_base). You need full control over the entire lifecycle, from defining the logic (create_transformation) to running the job at scale (bulk_run_pipeline). Don't use this if your goal is just simple text generation—if you only need a basic chatbot, simply creating one with create_chatbot might be enough. But for anything that requires structured data handling or state management, this MCP gives you the necessary operational levers.
Questions you might have
How do I manage multiple workflows with VectorShift (AI Workflow & RAG Automation)? +
You use the list_pipelines tool to see every workflow you've defined. You can then choose which one to run or modify using tools like get_pipeline.
Is VectorShift (AI Workflow & RAG Automation) good for batch processing? +
Yes, it handles volume well. Instead of running pipelines one by one, you can use the bulk_run_pipeline tool to execute multiple instances in parallel.
What's the difference between a chatbot and a knowledge base? +
A knowledge base (create_knowledge_base) is just the data repository. A chatbot requires you to create it using create_chatbot, which allows for active conversation management via tools like run_chatbot.
How do I stop a pipeline that's running too long? +
You use the terminate_pipeline tool. This immediately stops any workflow instance, preventing unnecessary resource consumption.
What data types can I use with the `index_knowledge_base` tool in VectorShift? +
It accepts multiple formats, including raw files and URLs. You simply point it at the source content, and VectorShift handles turning that material into searchable vectors for your knowledge base.
How secure is my data when I use VectorShift (AI Workflow & RAG Automation) with my AI client? +
Security relies on a zero-trust proxy for credentials. Your keys are never saved to disk, and every single tool call generates a cryptographically signed audit trail so you can trace exactly what happened.
What is the purpose of using the `create_transformation` tool in VectorShift? +
This tool lets you write custom logic using Python or JavaScript. You use it to clean, format, or manipulate data inputs, ensuring they are perfectly structured before a pipeline consumes them.
If I run a workflow with `run_pipeline`, can I monitor or pause its progress? +
Absolutely. The system tracks running pipelines for you. You have tools available to temporarily halt execution, like pause_pipeline, and then resume the process later without losing your state.
How do I search for specific information within my VectorShift knowledge base? +
Use the query_knowledge_base tool with your Knowledge Base ID and the search query. The agent will perform a semantic search and return the most relevant data chunks.
Can I trigger a specific AI workflow with custom parameters? +
Yes! Use the run_pipeline tool. Provide the Pipeline ID and a JSON object mapping your input names to their respective values to start the execution.
Is it possible to add new documents to a knowledge base through the agent? +
Absolutely. Use the index_knowledge_base tool to add data (such as URLs or file content) to an existing knowledge base for real-time RAG updates.
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