Does the agent connect directly to my database?

Yes. Your connection string is encrypted at rest and injected into an isolated runtime. The agent connects directly to your PostgreSQL instance — no intermediate proxies, no data copies, no third-party storage.

What vector dimensions are supported?

Any dimension supported by pgvector — from small 128-d vectors to large 3072-d embeddings (e.g., OpenAI text-embedding-3-large). Specify the dimension when creating a table and the agent handles the rest.

Which distance metrics can I use for similarity search?

pgvector supports three operators: `` (L2/Euclidean distance), `` (cosine distance), and `` (negative inner product). The agent uses cosine distance by default, which works best for normalized embeddings like those from OpenAI.

pgvector (Vector Database) MCP Server for Pydantic AI 6 tools — connect in under 2 minutes

Name: pgvector (Vector Database)
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect pgvector (Vector Database) through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to pgvector (Vector Database) "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in pgvector (Vector Database)?"
    )
    print(result.data)

asyncio.run(main())

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About pgvector (Vector Database) MCP Server

Connect your PostgreSQL + pgvector database to any AI agent and manage vector embeddings, similarity searches, and index optimizations through natural conversation.

Pydantic AI validates every pgvector (Vector Database) tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Vector Similarity Search — Run nearest-neighbor queries using cosine, L2, or inner product distance metrics across millions of embeddings with a single prompt.
Table Management — Discover which tables contain vector columns, create new embedding tables with custom dimensions, and inspect your schema.
Embedding CRUD — Insert, update, and delete individual vector entries with metadata, keeping your knowledge base fresh and accurate.
Index Optimization — Create HNSW or IVFFlat indexes on vector columns to accelerate approximate nearest-neighbor (ANN) queries by orders of magnitude.

The pgvector (Vector Database) MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect pgvector (Vector Database) to Pydantic AI via MCP

Follow these steps to integrate the pgvector (Vector Database) MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from pgvector (Vector Database) with type-safe schemas

Why Use Pydantic AI with the pgvector (Vector Database) MCP Server

Pydantic AI provides unique advantages when paired with pgvector (Vector Database) through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your pgvector (Vector Database) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your pgvector (Vector Database) connection logic from agent behavior for testable, maintainable code

pgvector (Vector Database) + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the pgvector (Vector Database) MCP Server delivers measurable value.

Type-safe data pipelines: query pgvector (Vector Database) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple pgvector (Vector Database) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query pgvector (Vector Database) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock pgvector (Vector Database) responses and write comprehensive agent tests

pgvector (Vector Database) MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect pgvector (Vector Database) to Pydantic AI via MCP:

create_index

Create vector index

create_table

Create vector table

delete_vector

Delete a vector

insert_vector

Insert a vector

list_tables

List tables

search_vectors

Vector similarity search

Example Prompts for pgvector (Vector Database) in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with pgvector (Vector Database) immediately.

"Show me all tables with vector columns in my database."

"Search for the 5 most similar documents to this query in the document_chunks table."

"Create a new table called 'support_tickets' with 1536-dimension vectors and an HNSW index."

Troubleshooting pgvector (Vector Database) MCP Server with Pydantic AI

Common issues when connecting pgvector (Vector Database) to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

pgvector (Vector Database) + Pydantic AI FAQ

Common questions about integrating pgvector (Vector Database) MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your pgvector (Vector Database) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.