How to Use the Indeed MCP in Pydantic AI

Get type-safe job listings in Pydantic AI with this MCP Server.

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Connect Indeed MCP to Pydantic AI

Create your Vinkius account to connect Indeed 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 Indeed with Pydantic AI

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Type-safe `search_jobs` for Pydantic AI

Your agent validates every response from the `search_jobs` tool against a Pydantic model. If the data doesn't match, the agent catches it immediately. This stops bad data from breaking your logic. It ensures your agent only acts on confirmed, valid job listings.

Reliable job data within Pydantic AI

Define your schema and let the agent enforce it during the search process. The `search_jobs` tool delivers data that conforms to your expectations. Integration is straightforward. Point your agent at the server and let the validation layer do the heavy lifting.

Secure agent actions with Pydantic AI

You control exactly what the agent sees and how it processes search results. The MCP Server acts as a clean bridge for your job data. No silent failures occur. If the search returns unexpected fields, your agent alerts you rather than proceeding with corrupted info.

Setup guide

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

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

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

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Common questions about Indeed MCP in Pydantic AI

Indeed, you can. By defining a model, you ensure that every job result from the server is validated before your agent touches it.

Indeed, the framework supports the HTTP and SSE transports required by this server. You just pass the toolset to your agent instance.

Indeed, the agent will throw a validation error. This prevents your workflow from continuing with bad data.

Yes. The MCP Server provides a model-agnostic interface, so your agent remains portable across different LLM backends.

The server uses a strict contract-based exchange. Every piece of data is validated against your schema, ensuring no external input can inject unwanted fields into your agent memory.

Use it with your favorite AI tools

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

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