MCP For Claude Marketplace
VectorShift (AI Workflow & RAG Automation)

VectorShift (AI Workflow & RAG Automation) MCP Connector for Claude

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Automate AI workflows and RAG via VectorShift — manage pipelines, query knowledge bases, and deploy chatbots directly from any AI agent.

29 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

Connect your VectorShift account to any AI agent and take full control of your AI automation and RAG (Retrieval-Augmented Generation) workflows through natural conversation.

What you can do

  • AI Pipelines — List, create, and run complex multi-step workflows. Manage execution with pause, resume, and terminate controls.
  • Knowledge Management — Create vector-based knowledge bases, index documents (files/URLs), and perform semantic searches to ground your AI.
  • Chatbot Orchestration — Deploy chatbots, upload context files, and run conversational instances directly.
  • Data Transformations — Execute custom data transformations and logic as part of your automated processes.

How it works

  1. Subscribe to this server
  2. Enter your VectorShift API Key
  3. Start building and running AI automations from Claude, Cursor, or any MCP-compatible client

Who is this for?

  • AI Developers — trigger and test RAG pipelines or knowledge base indexing straight from your coding environment
  • Operations Teams — automate repetitive data processing tasks using pre-built AI pipelines
  • Product Teams — quickly query internal knowledge bases to retrieve technical or product documentation via AI
ragai-workflowsautomationknowledge-basellm-ops

29 tools expose this connector's capabilities to your AI agent.

bulk_run_pipeline

Execute multiple instances of a pipeline in parallel

create_chatbot

Create a new chatbot

create_knowledge_base

Create a new knowledge base

create_pipeline

Create a new pipeline

create_transformation

Create a new transformation (Python/JS)

delete_chatbot

Delete a chatbot

delete_knowledge_base_documents

Delete specific documents by ID from a knowledge base

delete_knowledge_base

Delete a knowledge base

delete_pipeline

Delete a pipeline by ID

delete_transformation

Delete a transformation

get_chatbot

Fetch a chatbot by id or name

get_knowledge_base

Fetch a knowledge base by id or name

get_pipeline

Fetch a pipeline by id or name

get_transformation

Fetch a transformation by id or name

index_knowledge_base

Add data (files, URLs, etc.) to a knowledge base

list_chatbots

List all available chatbots

list_knowledge_base_documents

Find documents within a knowledge base

list_knowledge_bases

List all available knowledge bases

list_pipelines

List all available pipelines

list_transformations

List all available transformations

pause_pipeline

Pause a currently running pipeline instance

query_knowledge_base

Query a knowledge base with semantic search

resume_pipeline

Resume one or more paused pipeline instances

run_chatbot

Send a message to a chatbot and get a response

run_pipeline

Execute a pipeline with specified inputs

run_transformation

Execute a transformation with inputs

terminate_chatbot

Terminate an active chatbot session

terminate_pipeline

Stop a currently running pipeline instance

upload_chatbot_files

Upload files to a chatbot session

See how to talk to your AI agent using VectorShift (AI Workflow & RAG Automation).

List all my available VectorShift pipelines.

I've retrieved your pipelines. You have 3 active workflows: 'Customer Support Bot' (ID: pipe_1), 'Data Extractor' (ID: pipe_2), and 'Lead Scraper' (ID: pipe_3).

Search the 'Company Wiki' knowledge base (ID: kb_99) for 'remote work policy'.

Searching... I found relevant sections: 'Employees can work remotely up to 3 days a week' and 'Home office stipends are processed monthly'. Would you like more details?

Run the 'Data Extractor' pipeline (ID: pipe_2) with the input 'url: https://example.com'.

Pipeline 'Data Extractor' started. The execution is in progress. I will notify you once the data extraction from example.com is complete.

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.

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