How to Use the Exa MCP in Pydantic AI

Run type-safe neural web searches in Pydantic AI with Exa.

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Connect Exa MCP to Pydantic AI

Create your Vinkius account to connect Exa 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 Exa with Pydantic AI

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Validate neural search results in Pydantic AI

Pydantic AI guarantees that every response returned by `exa_search` matches your exact Python type hints at runtime. If the Exa search engine returns unexpected fields, the Pydantic AI framework raises a validation error immediately. This prevents your downstream code from processing corrupt or malformed Exa web data in your Pydantic AI application. You get clean, typed Exa search results that you can trust in production Pydantic AI workflows.

Type-safe similarity checks via MCP Server

Use `exa_find_similar` to discover related web pages while enforcing strict Pydantic AI schema validation on the output. Pydantic AI parses the returned Exa URLs and relevance scores into structured Python objects. This setup eliminates silent failures in your Pydantic AI data ingestion pipelines querying Exa. If the Exa MCP Server output deviates from your schema, your Pydantic AI application fails loud and fast.

Strict content extraction for structured agents

Extract clean text from target URLs using `exa_get_contents` without worrying about raw HTML breaking your Pydantic AI parser. Pydantic AI validates the extracted Exa text and highlights against your defined data models. This Exa MCP integration is perfect for building autonomous Pydantic AI agents that feed on verified web data. You get clean, structured Exa inputs that fit perfectly into your type-safe Pydantic AI pipelines.

Setup guide

Set up Exa 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": {
        "exa-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Exa MCP in Pydantic AI

Create an MCPToolset using the server's HTTP endpoint and pass it to your Agent. The toolset automatically maps the search tools to your agent's available tools.

The framework will raise a ValidationError at runtime. This ensures your agent never processes malformed web data or unexpected API payloads.

Yes, Pydantic AI is model-agnostic. You can run similarity searches with Anthropic, Gemini, or local models while maintaining strict type safety.

The unified MCPToolset is the modern, supported approach for external servers. It simplifies connection management and ensures reliable tool registration for content extraction.

Your search queries and target URLs are transmitted over encrypted TLS connections. Vinkius hosts the connector in an isolated, zero-trust environment, keeping your runtime data completely private.

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