How to Use the Redis Vector MCP in Pydantic AI

Q: Does this Redis Vector server support the Pydantic AI toolset approach?

Yes. You simply instantiate the MCPToolset with your server URL and pass it into your agent definition for type-safe interaction.

Q: Is it possible to perform batch operations with Redis Vector in Pydantic AI?

You can loop through your vector updates using upsertvector within your agent logic, relying on Pydantic to validate each item in the batch.

Get type-safe vector operations in Pydantic AI by connecting your agents to this Redis Vector server.

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Connect Redis Vector MCP to Pydantic AI

Create your Vinkius account to connect Redis Vector 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 Redis Vector with Pydantic AI

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Type-validated Redis Vector indexing for Pydantic AI

Define your index parameters and use `create_vector_index` to set up your Redis schema. Pydantic AI validates the tool inputs against your models to prevent runtime errors. `list_indexes` ensures your agent knows exactly which indexes are active. If an index configuration changes, your agent catches it immediately through validation.

Runtime-safe KNN search for Pydantic AI agents

Use `search_vectors` to fetch embeddings based on your search queries. The agent verifies that the float array matches your expected schema before the search hits Redis. This strict validation prevents the agent from hallucinating invalid search vectors. You get reliable, predictable results for your similarity searches.

Strict data management with Pydantic AI

The `upsert_vector` tool handles your data updates while Pydantic AI ensures the document key and vector payload follow your strict typing rules. If you need to clean up, `delete_vector` removes records safely. Your agent fails explicitly if a delete command targets an invalid key, preventing silent failures.

Setup guide

Set up Redis Vector 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": {
        "redis-vector-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Redis Vector MCP in Pydantic AI

Every tool response is parsed into a Pydantic model. If the Redis Vector server returns data that doesn't match your schema, the agent will throw a validation error.

Absolutely. By defining a Pydantic model for your search results, you ensure the agent only processes data that meets your strict requirements.

Yes. You simply instantiate the `MCPToolset` with your server URL and pass it into your agent definition for type-safe interaction.

You can loop through your vector updates using `upsert_vector` within your agent logic, relying on Pydantic to validate each item in the batch.

All vector data is confined to your ephemeral Vinkius sandbox. We use strict access control lists to ensure only your authorized agents can read or write to your Redis indexes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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