How to Use the Typesense Vector Search MCP in Pydantic AI

Q: Can I manage multiple indexes using Typesense Vector Search and Pydantic AI?

Yes. The listvectorcollections tool allows your agent to enumerate available collections. You then reference these structured names when calling tools like indexdocument.

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Connect Typesense Vector Search MCP to Pydantic AI

Create your Vinkius account to connect Typesense Vector Search to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Typesense Vector Search with Pydantic AI

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Creating structured collections via MCP Server

Start by defining your search schema using `create_collection`. You pass a JSON object, and the agent validates that structure before execution. This guarantees the collection adheres to strict rules, making sure subsequent tools operate on predictable data.

Executing searches with Pydantic AI

When you run `search_vectors`, every piece of data—the text query and the vector string—is checked against Pydantic models. If Typesense Vector Search returns unexpected output, your agent fails loudly, pointing out the error. This means you get reliable results; no silent corruption.

Managing document integrity in MCP Server

You can manage collections with `list_vector_collections` to see what's available. Need to update data? Use `index_document`. The agent ensures the new record matches the schema defined by your models. If you need details, `get_collection_details` returns structured metadata that Pydantic can validate immediately.

Setup guide

Set up Typesense Vector Search 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": {
        "typesense-vector-search-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Typesense Vector Search MCP in Pydantic AI

Pydantic provides a runtime layer of validation for the MCP Server calls. When you use `search_vectors`, your agent receives output that is guaranteed to match your Python models, eliminating guesswork.

The server touches document structures, including text fields and vector representations. Because of Pydantic's focus on correctness, the agent treats these components as strongly typed objects.

Yes. The `list_vector_collections` tool allows your agent to enumerate available collections. You then reference these structured names when calling tools like `index_document`.

No, the type safety doesn't restrict capability. You combine text filtering with vector search via `search_vectors`, and the resulting data is still validated against your predefined models.

The server touches document content (text and vectors). The agent's primary focus is on validating the structure of this data, ensuring that every operation respects the defined schema boundaries.

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