LanceDB MCP. Manage vectors and RAG without writing client code.
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LanceDB MCP gives your agent full control over serverless vector storage and RAG infrastructure. You can perform high-accuracy similarity searches, provision new columnar tables with precise schemas, and ingest multi-modal embeddings—all through natural conversation.
It’s how you manage complex vectorized data without writing manual Python scripts.
What your AI agents can do
Create table
Builds a new LanceDB table structure with a defined schema.
Delete table
Permanently removes an entire vector table from the database.
Get table
Retrieves the precise schema and metadata for a specific existing table.
Find semantically related rows by running highly-optimized K-Nearest Neighbor lookups against existing embeddings.
See every vectorized table in the database and retrieve its exact schema metadata, including vector dimensions.
Provision an entirely new columnar table, defining a precise Apache Arrow schema for your multi-modal AI data.
Insert new structured rows and their corresponding vectors into existing tables, updating the underlying ANN index automatically.
Irreversibly delete entire vector tables to maintain a clean, optimized database environment.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
LanceDB (Serverless Vector DB) MCP: 6 Tools
Use these tools to create, read, update, delete, list, and search your vectorized data structures with natural language commands.
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 LanceDB (Serverless Vector DB) on Vinkius019d75c4create table
Builds a new LanceDB table structure with a defined schema.
019d75c4delete table
Permanently removes an entire vector table from the database.
019d75c4get table
Retrieves the precise schema and metadata for a specific existing table.
019d75c4insert rows
Adds structured row payloads and vectors to a table, updating the ANN index in real time.
019d75c4list tables
Lists all vectorized tables that currently reside within your LanceDB instance.
019d75c4vector search
Runs an optimized K-Nearest Neighbor (KNN) search to find semantically related data.
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 LanceDB (Serverless Vector DB), then connect any of our 4,900+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,900+ 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 LanceDB. 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 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Debugging schema mismatch and table discovery is tedious.
Right now, if you want to know what vector tables exist or verify that a new field has the right dimension for embeddings, you're stuck in a manual cycle. You open your database console, run `list_tables`, check the output for typos, and then potentially run another command just to confirm the schema before writing any data.
With this MCP, you skip all the boilerplate. You simply ask your agent: 'What vector tables do I have?' The agent runs `list_tables` and gives you a clean list right in the chat window. Then, if you need details, it executes `get_table` for instant schema verification. It's that simple.
The LanceDB MCP lets you confidently build out your vector knowledge graph.
Before this MCP, building a new feature meant writing custom scripts to define the table first, ensuring all columns were typed correctly (e.g., defining the Float32 dimensions for vectors), and then carefully handling index initialization. If you messed up one type, the whole thing broke.
Now, your agent handles that complexity. You tell it what you need; it runs `create_table` using strict Apache Arrow definitions. Then, when you're ready to populate data, you just instruct it to run `insert_rows`, and the underlying index keeps up. That’s how reliable this process is now.
What you can do with this MCP connector
Managing structured vectors used for Retrieval-Augmented Generation (RAG) is usually a pain point. You're constantly scripting schema checks, running different similarity lookups, and manually managing which embeddings belong where. This MCP lets your agent handle all of that from natural conversation.
Instead of opening a local client or writing boilerplate Python code just to see what tables exist, you talk to the connection directly. Your agent handles connecting to the database endpoint, reading metadata, setting up schemas with specific Apache Arrow types, and keeping the underlying index current as you feed it new data.
Because Vinkius hosts this MCP, you connect once from any compatible client in the catalog, giving your workflow a central point for all vector operations.
019d75c4-1c1b-72ba-9873-ff0a3c0a9c4b 
How LanceDB MCP Works
- 1 Subscribe to this MCP on Vinkius and provide your LanceDB API URL, API Key, and Database Name.
- 2 Your agent connects using those credentials. It validates the connection and maps out all available vector tables.
- 3 You instruct your agent what you want—like 'search for documents about quantum physics' or 'create a new table for product metadata'—and it executes the necessary operations.
The bottom line is, you manage complex vector storage using natural language commands instead of writing client-side database queries.
Who Is LanceDB MCP For?
This MCP targets data professionals who spend too much time managing schema drift and manual indexing. It's for the RAG developer tired of running pip install just to check a table list, or the AI architect who needs a single source of truth for all stored vector topologies.
Uses the agent to perform semantic searches and verify document retrieval results without writing custom Python scripts.
Provisions and manages vector tables using strict Apache Arrow schemas, powering multi-modal AI applications.
Monitors vector topologies and audits storage usage across multiple serverless database instances efficiently.
What Changes When You Connect
- Avoid manual scripting for retrieval. Use
vector_searchto get relevant, semantically related results instantly, no boilerplate needed. - Define your data structure upfront. Use
create_tableto provision new vector tables with specific Apache Arrow schemas before any data goes in. - Keep your data clean and optimized. When a table is retired, use
delete_tableto permanently vaporize it, avoiding clutter. - Track everything easily. Run
list_tablesorget_tableto see the exact schema and metadata of any existing vector repository. - Process data in bulk. Use
insert_rowswhen you have a batch of records ready; the tool updates the underlying index automatically.
Real-World Use Cases
Analyzing old customer support documentation
A developer needs to find all articles related to 'API rate limits' across three different knowledge bases. Instead of running three separate search scripts, they ask their agent: 'Perform a vector search on the support_kb table for API rate limits.' The agent executes vector_search and returns the top results.
Building a new product catalog feature
An architect needs to store embeddings for newly digitized product manuals. They first tell their agent: 'Create a table named product_manuals with string IDs, text content, and vector fields.' The agent runs create_table, setting up the necessary structure before any data is inserted.
Cleaning up stale experimental data
A data engineer finishes a test using temporary embeddings. They tell their agent: 'I'm done with the old experiment table, delete it.' The agent runs delete_table, ensuring that useless data doesn't bloat the system and slow down future searches.
Inspecting a database before deployment
A team lead needs to verify the schema of an existing table called 'user_profiles'. They ask their agent, which runs get_table, providing immediate confirmation of the column types and vector dimensions without needing to query the API directly.
The Tradeoffs
Assuming basic insert is enough
The user just calls an insert function repeatedly, resulting in data that doesn't match the required schema or structure.
→
First, use create_table to provision the table with a strict Apache Arrow schema. Then, coordinate your writes by using insert_rows. This guarantees the underlying ANN index is properly initialized for all incoming payloads.
Trying complex joins
The user expects to join vector data with relational tables (e.g., joining a product ID in the vector table to an order number in another database).
→
This MCP focuses on pure vector operations. If you need cross-database logic, you must first use get_table to validate your schemas and then coordinate multiple data sources within your agent's workflow.
Forgetting the deletion step
A project finishes, but the temporary vector table remains in the database, wasting storage space.
→
Always run list_tables first to confirm its presence. When done, use delete_table. It's irreversible, so check twice.
When It Fits, When It Doesn't
Use this MCP if your primary need is managing vector data and RAG pipelines directly from your agent. You need a single interface to perform similarity searches (vector_search), manage schemas (create_table, get_table), and handle ingestion/cleanup for serverless vector storage. Don't use it if you primarily need to execute complex, multi-step transactional logic across completely separate database types (e.g., needing ACID compliance with a relational database). For those cases, integrate this MCP alongside a dedicated transaction management tool.
Basically: If the core problem involves vectors and embeddings, this is your go-to toolset.
Common Questions About LanceDB MCP
Can I perform a semantic similarity search using my agent? +
Yes. Use the vector_search tool by providing the target Table name and a JSON array of floating-point numbers representing your query embedding. Your agent will return the k-nearest rows from LanceDB based on semantic similarity.
How do I create a new table with a specific Apache Arrow schema? +
The create_table tool allows your agent to initialize a new columnar vector table. You just need to provide the desired Table name and a valid Apache Arrow schema mapping in JSON format defining dimensions and scalar fields.
Can my agent insert new embeddings directly into a LanceDB table? +
Absolutely. Use the insert_rows tool to persist new data rows containing native embeddings and arbitrary JSON metadata. Your agent will handle the payload delivery, and LanceDB will automatically update its ANN index.
Using `list_tables`, how do I audit which vector tables are currently active in my LanceDB instance? +
It provides an immediate, comprehensive list of all existing table names. This helps you quickly verify your database's current resource footprint and scope before making any changes.
What specific metadata can `get_table` provide for a vector table I plan to use? +
It delivers detailed schema information, including tensor dimensions, vector topologies, and the index type (like IVF-PQ). This is essential knowledge before running complex queries.
If I run `delete_table`, can I recover the data, or is the loss irreversible? +
The deletion process is irreversible. The action vaporizes the entire table structure and all associated vectors and rows immediately. Use this only when you are certain the data must be purged.
When I use `insert_rows`, does it guarantee that the underlying ANN index updates correctly? +
Yes, the process is designed for dynamic updating. The system handles inserting structured payloads and vectors while simultaneously refreshing all necessary components of the underlying ANN index.
How can I provision a new table using `create_table` if my data has non-standard dimensions? +
You must declare the specific Apache Arrow schema and multi-dimensional layout when calling create_table. This strict definition ensures your vector storage is structured for optimal AI workloads.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.