Skip to main content
AgentAccess
Meta AI / Llama57 Compatible Sources

Meta AI Integrations for Danish Systems

Integrate Meta AI agents and open-weights Llama models (Llama 3.3, 3.2, 3.1) with Danish public registers, CVR company data, DAWA addresses, and digital infrastructure via structured tool calling.

Official Meta AI Documentation
.md

Compatible Danish Sources (57)

Verified APIs, MCP servers, and data feeds ready for Meta AI integration.

Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for Bilbasen & Bilinfo API
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_bilbasen",
            "description": "Danish automotive marketplace and vehicle inventory APIs for car listings, valuations, and dealer syndication.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for Bilbasen & Bilinfo API",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in Bilbasen & Bilinfo API",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for Boliga.dk
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_boliga",
            "description": "Danish property sales history and valuation site with partially accessible data endpoints.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for Boliga.dk",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in Boliga.dk",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for CVR — Business Registry API
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_cvr-api",
            "description": "Central Business Register API giving machine access to all Danish companies, CVR numbers, ownership, and industry codes.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for CVR — Business Registry API",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in CVR — Business Registry API",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for Corti API
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_corti",
            "description": "Healthcare AI API for medical transcription, clinical documentation, coding, and agentic workflows.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for Corti API",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in Corti API",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for DAWA MCP Server (ITK)
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_mcp-dawa-itk",
            "description": "Official municipal MCP server for searching Danish addresses and geodata via DAWA.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for DAWA MCP Server (ITK)",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in DAWA MCP Server (ITK)",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for DAWA — Danish Addresses Web API
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_dawa",
            "description": "REST API for Danish addresses, postcodes, cadastre, and administrative geodata.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for DAWA — Danish Addresses Web API",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in DAWA — Danish Addresses Web API",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for DMI Open Data APIs
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_dmi-open-data",
            "description": "Meteorological, climate, ocean, radar, and lightning observation REST and OGC APIs.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for DMI Open Data APIs",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in DMI Open Data APIs",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for DR — Danish Broadcasting
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_dr",
            "description": "DR provides semi-official APIs for program listings, news content, and podcast feeds.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for DR — Danish Broadcasting",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in DR — Danish Broadcasting",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for Danmarks Miljøportal
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_danmarks-miljoeportal",
            "description": "National environmental data portal with web services and datasets across nature, water, climate, and land use.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for Danmarks Miljøportal",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in Danmarks Miljøportal",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for Danmarks Nationalbank — Data API
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_nationalbanken-data",
            "description": "The Danish central bank provides open statistical data including exchange rates, monetary data, and financial statistics.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for Danmarks Nationalbank — Data API",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in Danmarks Nationalbank — Data API",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for Danske Bank Developer Portal
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_danske-bank-api",
            "description": "Open Banking PSD2, Corporate Treasury, and FX REST APIs from Denmark’s largest bank.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for Danske Bank Developer Portal",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in Danske Bank Developer Portal",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Meta AI Integration Snippet
import os
from openai import OpenAI

# Initialize OpenAI-compatible client for Meta AI / Llama models
# (Compatible with Meta Llama Stack, Ollama, vLLM, Groq, or Together AI)
client = OpenAI(
    api_key=os.environ.get("LLAMA_API_KEY", "<YOUR_API_KEY>"),
    base_url=os.environ.get("LLAMA_API_BASE", "https://api.groq.com/openai/v1"),
)

# Meta AI / Llama Function Definition for Danske Bank Developer Portal
tools = [
    {
        "type": "function",
        "function": {
            "name": "query_danske-bank",
            "description": "Open banking and commercial APIs for account balances, transaction histories, and payment initiation.",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "Identifier or search query for Danske Bank Developer Portal",
                    },
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are Meta AI, equipped with function calling for Danish public registers and digital infrastructure.",
        },
        {
            "role": "user",
            "content": "Look up records in Danske Bank Developer Portal",
        },
    ],
    tools=tools,
)

print(response.choices[0].message)
Scaffold Custom Danish Tools

Access Enablers for Meta AI

Need to generate custom MCP servers, scrape portals, or run headless automations with Meta AI?

All Builder Enablers
FastMCP
MCP & CLI Builders
Open Source

High-level Python & TypeScript framework for building MCP servers

FastMCP provides clean decorators and microframework patterns to write production-ready MCP tools in under 10 lines of code.

#mcp#python#sdk
View
Firecrawl
Web Scraping & Clean MD
Freemium

Turn entire websites and portals into clean, LLM-ready Markdown

Firecrawl crawls, handles JavaScript rendering, bypasses anti-bot barriers, and outputs clean Markdown structured for AI agent ingestion.

#scraping#markdown#rag
View
Tavily Search API
Web Scraping & Clean MD
Freemium

Search engine & content extractor built exclusively for autonomous AI agents

Tavily is a purpose-built search engine designed for LLMs, delivering clean factual snippets and real-time web context with zero noise.

#search#agent-search#extraction
View

Frequently Asked Questions (Meta AI)

How do I connect Danish APIs to Meta AI and Llama models?

Use the structured Python function calling definition provided on this page. Meta Llama 3.1, 3.2, and 3.3 models support native function calling via the standard OpenAI-compatible tool schema used by Llama Stack, Ollama, vLLM, and hosted cloud providers.

Can Meta AI query live Danish registers like CVR or DAWA?

Yes. With function calling configured, Meta AI agents can execute live queries against CVR company records, DAWA address registers, DMI weather APIs, and public energy datasets in real time.

Does Meta AI require MitID or NemLog-in for Danish public data?

No. Open Danish public APIs require no citizen authentication. Any private workflow requiring MitID or NemLog-in must incorporate a human-in-the-loop (HITL) step.