How to Use the Fuzzy Match Search MCP in Pydantic AI

Q: I'm using a local model with Pydantic AI. Can I still use Fuzzy Match Search?

Yes, absolutely. The MCPToolset for Pydantic AI connects to this server independently of your LLM. Your local model can decide to call the fuzzymatch tool, and the request goes directly to the Vinkius server.

Q: How is my data secured when I use Fuzzy Match Search with Pydantic AI to fix strings?

The server that runs fuzzymatch is completely stateless. Your query string and the array of target strings you send are processed in a dedicated V8 Isolate sandbox that's destroyed after the request. No data persists.

Stop bad data before it corrupts your objects. Use Pydantic AI and Fuzzy Match Search to find the right string and validate it.

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Connect Fuzzy Match Search MCP to Pydantic AI

Create your Vinkius account to connect Fuzzy Match 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 Fuzzy Match Search with Pydantic AI

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Perplexity

Find the Correct Enum, Not a Close Guess

Your app uses a Pydantic model with a `Literal` type for status: 'IN_PROGRESS', 'COMPLETED', 'FAILED'. A user sends 'in progress'. Instead of writing complex normalization logic, your agent uses `fuzzy_match`. The tool finds 'IN_PROGRESS' as the top match. Your Pydantic AI agent can then use that validated string to construct the model, which passes validation. No more `ValidationError` from trivial case or spelling issues.

A Type-Safe MCP Server for Pydantic AI

This server is more than just a fuzzy-matching algorithm. The tool's output schema is well-defined. When your Pydantic AI agent calls `fuzzy_match`, the response is automatically parsed and validated against a Pydantic model. If this MCP Server ever returned an unexpected format, your agent would raise a `ValidationError` immediately. This prevents malformed data from propagating through your system. You get typo correction with a guarantee of structural correctness.

Reliable String Matching for Any LLM

You might be using GPT-4 today and a local Llama model tomorrow. Because Pydantic AI is model-agnostic, this tool works everywhere without changes. The `fuzzy_match` logic is completely decoupled from the LLM. Your agent's job is to decide *when* to ask for a correction. The `fuzzy_match` tool's job is to provide that correction reliably. This separation means your code is simpler and not tied to a specific model provider's quirks.

Setup guide

Set up Fuzzy Match 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": {
        "fuzzy-match-search-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Fuzzy Match Search transactions")
print(result.output)

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Common questions about Fuzzy Match Search MCP in Pydantic AI

By cleaning up string inputs before you try to create a Pydantic model. Use `fuzzy_match` to map a user's typo to a valid `Literal` or `Enum` member, ensuring your data fits the model's schema from the start.

Yes. The tool returns a ranked list of the strings you originally provided. Pydantic AI adds another layer by ensuring the structure of that list—for example, a list of objects with 'match' and 'score' keys—is always correct.

Yes, absolutely. The `MCPToolset` for Pydantic AI connects to this server independently of your LLM. Your local model can decide to call the `fuzzy_match` tool, and the request goes directly to the Vinkius server.

It's a perfect fit. You can build an agent that iterates through a messy dataset, using `fuzzy_match` to normalize a specific column against a known set of correct values. Pydantic models ensure each corrected record is valid.

The server that runs `fuzzy_match` is completely stateless. Your `query` string and the array of `target` strings you send are processed in a dedicated V8 Isolate sandbox that's destroyed after the request. No data persists.

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