How to Use the LanceDB (Serverless Vector DB) MCP in Pydantic AI

Q: How do I connect Pydantic AI to LanceDB (Serverless Vector DB)?

Instantiate MCPToolset with your Vinkius HTTP endpoint to register the MCP Server. Pass this to your Pydantic AI Agent's toolsets parameter to execute typed vector queries.

Q: How do I list and inspect tables using this MCP Server?

Your agent can call listtables on the MCP Server to get an array of active tables, then use gettable to retrieve the exact schema of a specific index before running queries.

Validate every vector search and table schema at runtime using Pydantic AI with LanceDB (Serverless Vector DB).

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Connect LanceDB (Serverless Vector DB) MCP to Pydantic AI

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Setup LanceDB (Serverless Vector DB) with Pydantic AI

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Type-Safe Vector Queries in Pydantic AI

The `vector_search` tool queries your serverless vector database and returns structured nearest-neighbor matches. Pydantic AI validates the returned distances and metadata payloads against your defined Python models at runtime. This validation guarantees that your Pydantic AI agent never processes malformed vector search results. If the LanceDB database schema changes, the framework raises a clear validation error instead of passing bad data to the LLM.

Strict Schema Enforcement and Table Inspection

The `create_table` tool builds new LanceDB vector tables with strict, typed schemas that match your Pydantic AI models. This ensures that every vector dimension and metadata field aligns with your application's expected types from day one. Your agent uses `get_table` to inspect existing indexes and verify field types within your Pydantic AI workflow. This prevents silent runtime failures when multiple Pydantic AI agents share the same serverless database via this MCP Server.

Validated Data Ingestion and Table Purging

The `insert_rows` tool writes structured payloads and embeddings directly into your LanceDB tables. Pydantic AI validates the data structure before sending the payload, ensuring that only clean records enter the vector index. When a table is no longer needed, your Pydantic AI agent can call `delete_table` to remove it. You can track active vector spaces using `list_tables` to keep your storage footprint clean.

Setup guide

Set up LanceDB (Serverless Vector DB) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "lancedb-serverless-vector-db-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to LanceDB (Serverless Vector DB) tools.",
)

result = await agent.run("List recent LanceDB (Serverless Vector DB) transactions")
print(result.output)

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Common questions about LanceDB (Serverless Vector DB) MCP in Pydantic AI

Instantiate `MCPToolset` with your Vinkius HTTP endpoint to register the MCP Server. Pass this to your Pydantic AI Agent's `toolsets` parameter to execute typed vector queries.

Pydantic AI will immediately raise a validation error at runtime. This prevents your agent from digesting unexpected LanceDB schemas or hallucinating fields based on bad vector metadata.

Yes, because Pydantic AI is model-agnostic, you can use local models or commercial APIs to generate embeddings, then store them using `insert_rows` and query them via `vector_search`.

Your agent can call `list_tables` on the MCP Server to get an array of active tables, then use `get_table` to retrieve the exact schema of a specific index before running queries.

All operations on your LanceDB tables run inside isolated, ephemeral V8 sandboxes. Your vector embeddings and credentials are never written to persistent shared storage, ensuring zero-trust data isolation.

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