> ## Documentation Index
> Fetch the complete documentation index at: https://clickhouse.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Como criar um agente de IA com Agno e o servidor MCP do ClickHouse
> Aprenda a criar um agente de IA com Agno e o servidor MCP do ClickHouse
Neste guia, você aprenderá a criar um agente de IA com o [Agno](https://github.com/agno-agi/agno) que pode interagir com
[o playground SQL do ClickHouse](https://sql.clickhouse.com/) usando [o servidor MCP do ClickHouse](https://github.com/ClickHouse/mcp-clickhouse).
**Notebook de exemplo**
Este exemplo pode ser encontrado como um notebook no [repositório de exemplos](https://github.com/ClickHouse/examples/blob/main/ai/mcp/agno/agno.ipynb).
## Pré-requisitos
* Você precisará ter o Python instalado no sistema.
* Você precisará ter o `pip` instalado no sistema.
* Você precisará de uma API key da Anthropic ou de uma API key de outro provedor de LLM
Você pode executar as etapas a seguir no REPL do Python ou por meio de um script.
Instale a biblioteca Agno executando os seguintes comandos:
```python theme={null}
pip install -q --upgrade pip
pip install -q agno
pip install -q ipywidgets
```
Em seguida, você precisará fornecer sua chave de API da Anthropic:
```python theme={null}
import os, getpass
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter Anthropic API Key:")
```
```response title="Response" theme={null}
Enter Anthropic API Key: ········
```
**Usando outro provedor de LLM**
Se você não tiver uma chave de API da Anthropic e quiser usar outro provedor de LLM,
poderá encontrar as instruções para configurar suas credenciais na [documentação da Agno](https://docs.agno.com/models/overview)
Em seguida, defina as credenciais necessárias para se conectar ao playground SQL do ClickHouse:
```python theme={null}
env = {
"CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
"CLICKHOUSE_PORT": "8443",
"CLICKHOUSE_USER": "demo",
"CLICKHOUSE_PASSWORD": "",
"CLICKHOUSE_SECURE": "true"
}
```
Agora, configure o servidor MCP do ClickHouse para apontar para o playground SQL do ClickHouse
e inicialize também o nosso agente Agno e faça uma pergunta a ele:
```python theme={null}
from agno.agent import Agent
from agno.tools.mcp import MCPTools
from agno.models.anthropic import Claude
```
```python theme={null}
async with MCPTools(command="uv run --with mcp-clickhouse --python 3.13 mcp-clickhouse", env=env, timeout_seconds=60) as mcp_tools:
agent = Agent(
model=Claude(id="claude-3-5-sonnet-20240620"),
markdown=True,
tools = [mcp_tools]
)
await agent.aprint_response("What's the most starred project in 2025?", stream=True)
```
```response title="Response" theme={null}
▰▱▱▱▱▱▱ Thinking...
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ What's the most starred project in 2025? ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ • list_tables(database=github, like=%) ┃
┃ • run_select_query(query=SELECT ┃
┃ repo_name, ┃
┃ SUM(count) AS stars_2025 ┃
┃ FROM github.repo_events_per_day ┃
┃ WHERE event_type = 'WatchEvent' ┃
┃ AND created_at >= '2025-01-01' ┃
┃ AND created_at < '2026-01-01' ┃
┃ GROUP BY repo_name ┃
┃ ORDER BY stars_2025 DESC ┃
┃ LIMIT 1) ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (34.9s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ To answer your question about the most starred project in 2025, I'll need to query the ClickHouse database. ┃
┃ However, before I can do that, I need to gather some information and make sure we're looking at the right data. ┃
┃ Let me check the available databases and tables first.Thank you for providing the list of databases. I can see ┃
┃ that there's a "github" database, which is likely to contain the information we're looking for. Let's check the ┃
┃ tables in this database.Now that we have information about the tables in the github database, we can query the ┃
┃ relevant data to answer your question about the most starred project in 2025. We'll use the repo_events_per_day ┃
┃ table, which contains daily event counts for each repository, including star events (WatchEvents). ┃
┃ ┃
┃ Let's create a query to find the most starred project in 2025:Based on the query results, I can answer your ┃
┃ question about the most starred project in 2025: ┃
┃ ┃
┃ The most starred project in 2025 was deepseek-ai/DeepSeek-R1, which received 84,962 stars during that year. ┃
┃ ┃
┃ This project, DeepSeek-R1, appears to be an AI-related repository from the DeepSeek AI organization. It gained ┃
┃ significant attention and popularity among the GitHub community in 2025, earning the highest number of stars ┃
┃ for any project during that year. ┃
┃ ┃
┃ It's worth noting that this data is based on the GitHub events recorded in the database, and it represents the ┃
┃ stars (WatchEvents) accumulated specifically during the year 2025. The total number of stars for this project ┃
┃ might be higher if we consider its entire lifespan. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
```