How to Use the pgvector (Vector Database) MCP in Pydantic AI

Build type-safe AI pipelines in Pydantic AI with native pgvector (Vector Database) integration.

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Connect pgvector (Vector Database) MCP to Pydantic AI

Create your Vinkius account to connect pgvector (Vector Database) to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup pgvector (Vector Database) with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Type-validated vector operations

Use `insert_vector` to add new embeddings to your tables. Pydantic AI validates the payload against your models, ensuring no malformed data reaches your database. If a tool call returns unexpected output, the agent throws a validation error immediately. This prevents silent failures in your retrieval pipeline.

Query your data with Pydantic AI

Run `search_vectors` to find relevant information during your agent's execution. It returns results that match your strict type definitions. This makes it easy to map database rows directly into your application objects. You get clean code and reliable query results every time.

Manage your index with MCP Server

Configure your vector indexes using `create_index` within your agent's setup phase. It is a predictable way to prepare your database for production traffic. Use `create_table` to initialize your schema if it does not exist. Your agent handles these structural changes with full type safety.

Setup guide

Set up pgvector (Vector Database) 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": {
        "pgvector-vector-database-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent pgvector (Vector Database) transactions")
print(result.output)

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Common questions about pgvector (Vector Database) MCP in Pydantic AI

Absolutely. Every response from the server is validated against your Pydantic models. If the schema mismatches, the agent stops and reports the issue.

By forcing all interactions through typed tools, you ensure that only valid vectors are inserted or searched. This reduces the risk of runtime errors in your agent logic.

Yes. It supports stable HTTP transports and provides a reliable way to connect your database to your agent. It is designed for production reliability.

The server uses a dedicated endpoint token. Only authorized clients with the correct key can trigger database tools.

The tool returns a clear error message. Your agent can then decide whether to ignore it or log a warning.

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