How to Use the Exa AI MCP in Pydantic AI

Q: Can I use Exa AI to filter results by crawl date in Pydantic AI?

Yes, the searchbycrawldate tool is fully compatible. You can pass your date parameters into the tool and receive validated results back.

Q: Is the Exa AI connection stable when using Pydantic AI?

The server includes getapistatus for verification. You can integrate this into your startup checks to confirm the connection before the agent runs.

Get type-safe search results from Exa AI inside your Pydantic AI agent.

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Connect Exa AI MCP to Pydantic AI

Create your Vinkius account to connect Exa AI 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 AI with Pydantic AI

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Type-safe MCP Server for Pydantic AI

Every response from the twelve available tools is validated against your Pydantic models. If the data structure doesn't match, your agent fails immediately rather than processing bad data. This architecture prevents silent failures. You define the schema, and the server ensures the search results conform to it perfectly.

Precise extraction with Pydantic AI

Use `search_with_contents` to get structured data back from the web. Because you are using Pydantic AI, you can enforce strict requirements on the content returned by the server. If the page content is missing expected fields, the validation layer catches it. This gives you confidence in the data your agent uses for decision-making.

Reliable search tools for your MCP Server

Register the server using `MCPToolset` to gain access to the full suite of search tools. The integration supports both SSE and HTTP transports for stable communication. You can trust that `keyword_search` and other tools will return consistent data formats. This makes your agent's logic cleaner and easier to debug.

Setup guide

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

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

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

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Common questions about Exa AI MCP in Pydantic AI

You define a Pydantic model that matches the expected response shape. Pydantic AI will validate the output of the `semantic_search` tool against this model.

The framework will throw a validation error. This prevents your agent from hallucinating or acting on improperly formatted search results.

Yes, the `search_by_crawl_date` tool is fully compatible. You can pass your date parameters into the tool and receive validated results back.

The server includes `get_api_status` for verification. You can integrate this into your startup checks to confirm the connection before the agent runs.

Data privacy is enforced through strict input validation and secure transport protocols. The server only handles the specific queries you execute, and no search data is persisted.

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