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For agents:start at/llms.txt·everything as Markdown at/llms-full.txt·or add theMCP server.

Use Oro Intel with PydanticAI

Give a PydanticAI agent live UK tender and contract data with MCPServerStreamableHTTP pointed at Oro Intel's remote MCP server.

Prerequisites

  • An Oro Intel API key in ORO_API_KEY (create one, 250 free credits).
  • An LLM key (this example uses Anthropic via ANTHROPIC_API_KEY).
  • pip install "pydantic-ai-slim[mcp,anthropic]"

Code

agent.py
"""PydanticAI agent with live UK procurement data via the Oro Intel remote MCP server."""

import asyncio
import os

from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

ORO_API_KEY = os.environ["ORO_API_KEY"]  # https://app.oro-intel.com/dashboard/developers

server = MCPServerStreamableHTTP(
    url="https://api.oro-intel.com/mcp/",  # trailing slash required
    headers={"Authorization": f"Bearer {ORO_API_KEY}"},
)

agent = Agent("anthropic:claude-sonnet-5", toolsets=[server])


async def main() -> None:
    async with agent:
        result = await agent.run(
            "Look up the UK company 'Serco' and give me its full supplier profile. "
            "Report how many public contracts it has won, and the credits_charged "
            "and credits_remaining from the tool responses."
        )
    print(result.output)


asyncio.run(main())

Expected output

output (trimmed)
Serco Limited (company number 02048608) has won 132 public contracts.
Lookup: 5 credits; full profile: 12 credits (credits_remaining: 233).

The agent chains oro_lookup_company (5 credits) into oro_company_profile (12 credits); each tool response includes credits_charged/credits_remaining.

Next steps