How accurate is the geocoding — can I use it for delivery routing?

OpenCage returns a confidence score from 1 to 10 for each result. Use the high-confidence filter (min_confidence=8 or higher) to get delivery-grade accuracy. Combined with country filtering, you can eliminate ambiguous matches and get coordinates reliable enough for last-mile dispatch.

What does privacy mode actually do?

When you enable no_record=1, OpenCage processes your query but doesn't log it in their cache or analytics. The result is identical, but the query leaves no trace on their servers. Ideal for geocoding patient addresses, legal documents, or any PII-sensitive data.

Can I geocode in multiple languages — like getting results in Portuguese for a Brazilian address?

Yes. Use the language bias tool with any IETF language code (pt-BR, es-MX, fr-FR, etc.) and OpenCage returns the address components translated to that locale. Perfect for building multilingual apps or standardizing address formats across regions.

OpenCage MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect OpenCage through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to OpenCage "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in OpenCage?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About OpenCage MCP Server

Give your AI agent precise geolocation superpowers with OpenCage Geocoding. Convert any address into coordinates, reverse-geocode GPS pins into readable addresses, and apply advanced filters — all through natural conversation.

Pydantic AI validates every OpenCage tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Forward Geocoding — Convert any address or place name into exact latitude/longitude coordinates
Reverse Geocoding — Turn GPS coordinates into structured street addresses with timezone and sun data
Country Filtering — Restrict results to a specific country (ISO 3166-1 Alpha-2) to avoid ambiguous city matches
Language Bias — Request results localized in any IETF language code (e.g., pt-BR, fr-FR)
Confidence Scoring — Filter geocoding results by minimum confidence level (1–10) for delivery-grade accuracy
Bounding Box — Constrain results to a geographic rectangle for targeted regional searches
Privacy Mode — Run geocoding queries without OpenCage logging them, for sensitive addresses
Duplicate Control — Return or suppress duplicate results for data validation workflows

The OpenCage MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect OpenCage to Pydantic AI via MCP

Follow these steps to integrate the OpenCage MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from OpenCage with type-safe schemas

Why Use Pydantic AI with the OpenCage MCP Server

Pydantic AI provides unique advantages when paired with OpenCage through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your OpenCage integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your OpenCage connection logic from agent behavior for testable, maintainable code

OpenCage + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the OpenCage MCP Server delivers measurable value.

Type-safe data pipelines: query OpenCage with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple OpenCage tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query OpenCage and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock OpenCage responses and write comprehensive agent tests

OpenCage MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect OpenCage to Pydantic AI via MCP:

geocode_all_duplicate_results

Retrieve the exact structural matching verifying Delivery alternatives

geocode_basic

Identify bounded routing spaces inside the Headless OpenCage Engine

geocode_bounding_box

Inspect deep internal arrays mitigating specific Polygon domains

geocode_country_filter

Perform structural extraction of properties driving active Country nodes

geocode_high_confidence

Dispatch an automated validation check routing explicit Score limits

geocode_language_bias

Retrieve explicit Cloud logging tracing explicit Payload locales

geocode_no_record_privacy

Provision a highly-available JSON Payload generating secure mappings

reverse_basic

Enumerate explicitly attached structured rules exporting active GPS pins

reverse_fast_no_annotations

Identify precise active arrays spanning native Location limits faster

reverse_fetch_time_annotations

Irreversibly vaporize explicit validation limits extracting UTC logic

Example Prompts for OpenCage in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with OpenCage immediately.

"What are the coordinates for 1600 Amphitheatre Parkway, Mountain View, CA?"

"What's at coordinates 48.8566, 2.3522?"

"Geocode 'Springfield' but only show results in the United States with confidence >= 7."

Troubleshooting OpenCage MCP Server with Pydantic AI

Common issues when connecting OpenCage to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

OpenCage + Pydantic AI FAQ

Common questions about integrating OpenCage MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your OpenCage MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.