GroundX MCP for AI. Connect Your AI to Private Knowledge Bases
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How this MCP server connects to your AI agent
GroundX is an MCP for connecting your AI agent directly to private enterprise data stores. It lets you index massive amounts of documents—from local files or entire websites—and run them through a Retrieval-Augmented Generation (RAG) pipeline.
Forget generic web searches; this tool gives your AI client access to your company's specific knowledge, turning unstructured PDFs and internal wikis into actionable context for answering questions.
What AI agents can do with GroundX Automation
Create bucket
Sets up a new container where you can store and categorize documents.
Create group
Organizes multiple buckets into a single logical collection for easier management.
Get customer info
Retrieves specific account and customer details needed for context during searches.
You can send documents by URL or local path for the system to ingest.
The MCP crawls a given website and ingests all the content it finds there.
You can perform semantic searches across all ingested data to find relevant chunks of text.
This allows you to search for specific files based on their content or associated metadata.
You can list and create the dedicated containers (buckets) where different sets of documents are stored.
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What AI agents can do with GroundX MCP with 12 Tools
These tools let you manage the entire lifecycle of your knowledge base: from creating buckets to ingesting files and running complex searches.
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 GroundX on VinkiusCreate Bucket
Sets up a new container where you can store and categorize documents.
Create Group
Organizes multiple buckets into a single logical collection for easier management.
Get Customer Info
Retrieves specific account and customer details needed for context during searches.
Get Ingest Status
Checks if a document ingestion task is finished processing or still running.
Ingest Documents
Loads documents into the system using provided URLs or paths.
Ingest Website
Crawls and pulls all textual content from a given website address.
List Buckets
Shows you every container (bucket) you have set up for storing documents.
List Content
Lists all the individual documents that have been successfully indexed into the...
List Groups
Displays all organized groupings of buckets you've created.
List Workflows
Lists the predefined pipelines used to manage complex retrieval and indexing...
Search Content
Performs a deep, conceptual search across all your indexed knowledge.
Search Documents
Finds specific documents by querying their metadata or content directly.
Security and governance baked right in.
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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:
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Replace [YOUR_TOKEN_HERE] with your token
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Start a conversation
Open a new chat. The GroundX 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
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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 12 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Finding Policies Requires Clicking Through Five Different Intranets, Solved with Vinkius AI Gateway
Right now, if you need to know the refund policy or the latest PTO guidelines, you open three different internal wikis, click through departments, and copy-paste chunks of text into a document just so your team can read it. It's slow, it’s fragmented, and nobody remembers where they copied the last piece.
With this MCP, everything lives in one place. You feed all those disparate sources—HR manuals, support FAQs, product specs—into GroundX. Then, when someone asks about PTO, your AI agent just searches the unified knowledge base and spits out one definitive answer.
Search Content
You don't have to manually search multiple document types or run separate queries for different policy areas. You simply call `search_content`, pointing the agent at the entire knowledge base, and it handles the indexing logic behind the scenes.
The difference is that your agent stops being a general chatbot; it becomes an expert on your business.
What your AI can actually do with this
Your AI agent needs more than just general internet knowledge; it needs to know what lives inside your organization. That’s where GroundX comes in. This MCP lets you treat all your private data—PDFs, manuals, support tickets, everything—as a searchable source of truth for your LLM calls. You can feed documents into the system via URLs or local paths and then tell the agent to search across them.
Need to keep track of what's indexed? The toolset lets you list all content buckets and check ingestion progress with status checks. If you build agents using Vinkius, this MCP gives your client a dedicated pathway to your proprietary knowledge base. It turns raw data into context, allowing the agent to answer questions based on your policies and your product specs, not just general training data.
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Here's how it actually works
The bottom line is you tell your AI agent where the data lives and what questions it needs to answer, and this MCP handles the rest of the indexing and retrieval work.
First, use a function to establish your data structure by creating new content buckets.
Next, feed the MCP with data—either running ingest_documents from files or using list_workflows to manage complex retrieval pipelines.
Finally, send search queries that trigger semantic searches across all indexed content.
Who is this actually for?
This connector is for the Knowledge Manager who spends hours manually searching multiple internal wikis just to find one policy number. It's also built for Data Scientists needing a stable, repeatable way to connect LLMs to proprietary datasets without building complex ETL pipelines from scratch.
They use the MCP to define buckets and manage groups of data, ensuring that all company knowledge is correctly organized for retrieval.
They integrate this MCP into agent code, calling tools like search_content to ensure their AI application always answers based on the latest internal documentation.
What Changes When You Connect
Stop relying on general web knowledge. By using ingest_documents or ingest_website, your agent searches only the documents you control, making answers specific and reliable.
You don't have to rebuild indexing logic every time. The MCP lets you list all available workflows (list_workflows) and use them to manage complex data pipelines without writing custom code.
Need context about a client? Use get_customer_info within your agent’s prompt flow. This gives the AI relevant account details when it generates an action or answer.
When you need to know if the data is ready, use get_ingest_status. It tells you exactly where the process stands—finished, failed, or still processing.
GroundX lets you structure your knowledge by using create_bucket and then grouping them with list_groups, making sure related documents are always searched together.
See it in action
Handling a Product Inquiry
A customer service agent needs to answer questions about warranty changes. Instead of guessing, they call the GroundX MCP's search_content tool, pointing it at the 'Warranty Policy' bucket, ensuring the AI uses only the most current company documents for its response.
Onboarding New Employees
A new employee needs to know the internal HR policy. The agent calls ingest_documents with the latest handbook PDFs and then runs a query that uses list_buckets to confirm all relevant source material is indexed before answering.
Analyzing Competitor Data
A market analyst wants to compare internal specs against public data. They use ingest_website on competitor sites, then run a targeted search using search_documents to pull out specific features for comparison.
Debugging an Agent Flow
A developer needs to know which documents are available to the agent. They first use list_buckets and then check list_content to confirm that all required data sources were uploaded correctly before testing.
The honest tradeoffs
Using generic search
Just pasting a question into the agent without context, hoping it 'just knows' the answer.
Always use search_content or search_documents. First, ensure your data is loaded by using ingest_documents and then pass the query to the MCP. This guarantees grounding.
Forgetting document status
Running a search immediately after calling ingest_website, assuming the content is ready, only for the agent to fail.
Check the processing pipeline first by running get_ingest_status. Don't query until that tool confirms the task is complete.
Over-indexing everything
Throwing all company data into one bucket, making it impossible to track which source provided which piece of information.
Use create_bucket and list_groups to segment your knowledge base. Group related policies together for clearer retrieval.
When It Fits, When It Doesn't
Use this MCP if the core problem is 'How do I get my AI agent to talk about my company data?' If you need general web context, stick with a standard search engine connection. If your goal is simply to store files without making them searchable for an LLM, a basic cloud storage solution works fine—but it won't execute the retrieval step. You need GroundX when you need the AI to read and synthesize knowledge from structured or unstructured private documents; that’s what combining list_buckets, ingest_documents, and search_content achieves.
Questions you might have
How do I get my documents into GroundX using ingest_documents? +
You provide the tool with the URLs or local file paths. The MCP handles the actual loading and indexing process for you, so you don't have to write any upload scripts.
What is the difference between search_content and search_documents? +
Search content performs a conceptual search across everything indexed. Search documents lets you narrow down your query by looking at metadata or searching for a specific file container.
Can I crawl an entire website using ingest_website? +
Yes, ingest_website crawls the specified URL and pulls all textual content it finds. This is much faster than manual copy-pasting or uploading dozens of individual pages.
How do I ensure my data is ready before searching? +
You must check the processing status using get_ingest_status. The MCP won't search until that tool confirms all ingestion tasks are complete.
What is the best way to organize my data sources using `create_bucket` and `list_buckets`? +
You first call list_buckets to see all existing containers. Then, use create_bucket to isolate new document sets by topic or department. This keeps your knowledge base structured and easy for the agent to target.
Where can I check the status or name of my automated data pipelines with `list_workflows`? +
list_workflows shows all defined RAG pipelines within GroundX. This lets you verify that your complex document processing chains are active and properly configured before attempting a search.
How can I monitor a large data upload job and confirm it finished processing with `get_ingest_status`? +
Use get_ingest_status to poll the ingestion job's status. It provides a definitive confirmation of whether your document task succeeded, failed, or is still running in the background.
After running an ingest job, what does `list_content` show me about my documents? +
It shows a full manifest of every indexed document. This list provides critical details like timestamps and source file names, confirming exactly what data is available for your agent to search.
How do I query my indexed documents? +
Simply ask the AI agent to search for a specific term or concept, and it will query the GroundX API to retrieve the most relevant textual chunks.
Can I manage data buckets from the agent? +
Yes, you can list your active buckets, check their document count, and verify index status.
Does it support adding new files to a bucket? +
Currently, the integration focuses on querying the optimized indexes. File ingestion should be managed through the GroundX dashboard or a separate pipeline.
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