# Georef Argentina MCP

> Georef Argentina accesses official Argentine geographic data, letting your AI client validate and structure location information. It normalizes messy addresses into precise components; lists administrative divisions like provinces and departments; and performs reverse geocoding on coordinates. This MCP is essential for any app dealing with shipping or large-scale datasets in Argentina.

## Overview
- **Category:** data-management
- **Price:** Free
- **Tags:** geocoding, administrative-data, argentina, address-normalization, public-records, spatial-data

## Description

Need to deal with Argentine locations? Raw address strings are a nightmare, and simple coordinate pairs don't tell you the full story. This connector lets your agent talk directly to national geographic databases. You can feed it an unstructured street name and get it broken down into official components; or you can give it coordinates and have it identify the exact administrative region, like the neighborhood or municipality. It’s about making messy data usable for shipping labels or analytics tables. When building complex systems, connecting through Vinkius makes this reliable geographic dataset available to your AI agent without needing a dedicated backend microservice. You just ask your client—Claude, Cursor, any MCP-compatible client—to validate the location, and it handles all the complexity.

## Tools

### get_departamentos
Retrieves a list of all departments found within Argentina.

### normalize_direccion
Takes an unstructured address string and separates it into its accurate, standardized components.

### get_localidades
Provides a comprehensive list of localities across Argentina.

### get_municipios
Gets a full listing of municipalities in Argentina.

### get_provincias
Retrieves and filters official provinces by name or ID from Argentina.

### reverse_geocoding
Identifies the administrative location (province/municipality) corresponding to a set of latitude and longitude coordinates.

### get_vias
Fetches a complete list of streets (vías) for any given location in Argentina.

## Prompt Examples

**Prompt:** 
```
List all provinces in Argentina.
```

**Response:** 
```
I've retrieved the list of provinces. Argentina has 24 main jurisdictions, including Buenos Aires, Córdoba, Santa Fe, and Mendoza. Would you like to see the IDs or centroids for any of them?
```

**Prompt:** 
```
Normalize the address 'Av. 9 de Julio 1234, CABA'.
```

**Response:** 
```
The address has been normalized. It corresponds to 'AVENIDA 9 DE JULIO 1234', located in the Autonomous City of Buenos Aires (CABA). The coordinates are Lat: -34.612, Lon: -58.381.
```

**Prompt:** 
```
What is the location for coordinates -34.6037, -58.3816?
```

**Response:** 
```
Those coordinates correspond to the Obelisco area in the Autonomous City of Buenos Aires (CABA), specifically within the San Nicolás neighborhood.
```

## Capabilities

### Structure messy addresses
Converts raw text strings into structured data with specific components like street name and number.

### List administrative boundaries
Retrieves lists of official provinces, departments, municipalities, or localities within Argentina.

### Determine location from coordinates
Takes latitude and longitude to return the precise administrative area the point falls into (reverse geocoding).

### Search specific streets
Finds a list of known streets or 'vías' within defined localities.

## Use Cases

### Handling a new delivery manifest
A logistics manager receives 100 addresses from a client. Instead of manually checking every single one, they ask their agent to run the entire batch through `normalize_direccion`. The MCP returns clean, structured data for all 100 points, identifying which ones are incomplete or invalid.

### Mapping historical census data
A data analyst has old records with just latitude and longitude points. They use `reverse_geocoding` to map those raw coordinates back to the specific municipality they belong to, allowing them to accurately segment their analysis.

### Building a location search feature
A developer needs to build a form that guides users through selecting an area. They use `get_provincias` first, then `get_departamentos`, and finally `get_municipios` to create a multi-layered selection process powered by official lists.

### Verifying street existence
A developer needs to confirm if an address is on a recognized road. They use the MCP’s tools to first find the relevant locality, then call `get_vias` to check against the official list of streets.

## Benefits

- Stop guessing about addresses. Running a full street string through `normalize_direccion` breaks it down into components that match official government records, which is critical for shipping label accuracy.
- No more manual lookups for regions. If you only have coordinates, use `reverse_geocoding`. The MCP immediately tells you the exact municipality and province without needing a separate map service call.
- Need to verify boundaries? Use `get_provincias` or `get_departamentos` to pull canonical lists of administrative areas. This guarantees your data structure matches Argentina's official schema.
- Build smarter logistics tools by using `get_municipios` and `get_localidades`. You can list every possible jurisdictional grouping, ensuring your application covers the entire country.
- Improve data integrity across large datasets. By systematically running records through the MCP, you ensure that all location fields are standardized before analysis begins.

## How It Works

The bottom line is that you get clean, validated, and government-sourced location data directly into your workflow.

1. First, subscribe to this MCP and provide your necessary API key credentials.
2. Next, direct your AI agent to perform the required location action (e.g., 'normalize address X' or 'list provinces').
3. The agent executes the tool call, receiving structured JSON data containing the official Argentine geographic details.

## Frequently Asked Questions

**How do I validate a messy address string using normalize_direccion?**
You pass the raw address text to `normalize_direccion`. It automatically breaks it into separate, official components like street name and number. This is much better than trying to parse it yourself.

**Can I find out what province an arbitrary coordinate belongs to using reverse_geocoding?**
Yes. Simply run `reverse_geocoding` with the latitude and longitude. It will tell you exactly which administrative region, down to the municipality, that point falls within.

**Which tool should I use if I just want a list of all provinces?**
Use `get_provincias`. This tool is dedicated solely to listing and filtering the official 24 main jurisdictions in Argentina, ensuring you get a complete and accurate set.

**Does this MCP handle both department and municipality lookups?**
Yes. You have specific tools for that: use `get_departamentos` to list departments, or `get_municipios` when you need the full roster of municipalities.

**If I'm running high-volume tasks like normalizing addresses with normalize_direccion, do I need an API key?**
Yes, you may require a dedicated Georef API Key for high-volume access. The documentation provides details on setup and usage limits for robust integration.

**Can the get_departamentos tool filter results by name or ID?**
The list of departments can be filtered using either a specific department's name or its unique ID. This lets you pull exact data without sifting through entire regional lists.

**What information do I need to provide when calling the get_vias tool?**
You must specify the locality or department where the street is located. This scope limits the search and ensures that get_vias only returns relevant streets (vías) for your area.

**When I use reverse_geocoding, what administrative context does the output provide?**
The output provides not just a geographic match, but also the associated administrative location. You get clear identification of the neighborhood and locality tied to those specific coordinates.

**Can I find the coordinates of a specific address string?**
Yes! Use the `normalize_direccion` tool. It will parse the address and return the structured components along with geographic coordinates.

**How do I list all departments within a specific province?**
Use the `get_departamentos` tool and provide the province name or ID in the `provincia` parameter to filter the results.

**Can I get the administrative location of a specific latitude and longitude?**
Yes, the `reverse_geocoding` tool takes `lat` and `lon` parameters and returns the corresponding province, department, and municipality.