示例 notebook你可以在示例仓库中找到该示例对应的 notebook。
前置条件
- 你的系统中需要已安装 Python。
- 你的系统中需要已安装
pip。 - 你需要一个 OpenAI API 密钥
1
安装库
运行以下命令安装
mcp-agent 库：pip install -q --upgrade pip
pip install -q mcp-agent openai
pip install -q ipywidgets
2
设置凭据
接下来，您需要提供 OpenAI API 密钥：接下来，配置连接到 ClickHouse SQL playground 所需的凭据：
import os, getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter OpenAI API Key:")
Response
Enter OpenAI API Key: ········
env = {
"CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
"CLICKHOUSE_PORT": "8443",
"CLICKHOUSE_USER": "demo",
"CLICKHOUSE_PASSWORD": "",
"CLICKHOUSE_SECURE": "true"
}
3
初始化 MCP server 和 mcp-agent agent
现在将 ClickHouse MCP 服务器配置为指向 ClickHouse SQL playground，
然后初始化 agent 并向其提问：
from mcp_agent.app import MCPApp
from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
from mcp_agent.config import Settings, MCPSettings, MCPServerSettings, OpenAISettings
settings = Settings(
execution_engine="asyncio",
openai=OpenAISettings(
default_model="gpt-5-mini-2025-08-07",
),
mcp=MCPSettings(
servers={
"clickhouse": MCPServerSettings(
command='uv',
args=[
"run",
"--with", "mcp-clickhouse",
"--python", "3.10",
"mcp-clickhouse"
],
env=env
),
}
),
)
app = MCPApp(name="mcp_basic_agent", settings=settings)
async with app.run() as mcp_agent_app:
logger = mcp_agent_app.logger
data_agent = Agent(
name="database-anayst",
instruction="""You can answer questions with help from a ClickHouse database.""",
server_names=["clickhouse"],
)
async with data_agent:
llm = await data_agent.attach_llm(OpenAIAugmentedLLM)
result = await llm.generate_str(
message="Tell me about UK property prices in 2025. Use ClickHouse to work it out."
)
logger.info(result)
Response
[10/10/25 11:26:20] INFO Starting MCP server 'mcp-clickhouse' with transport 'stdio' server.py:1502
2025-10-10 11:26:20,183 - mcp.server.lowlevel.server - INFO - Processing request of type ListToolsRequest
2025-10-10 11:26:20,184 - mcp.server.lowlevel.server - INFO - Processing request of type ListPromptsRequest
2025-10-10 11:26:20,185 - mcp.server.lowlevel.server - INFO - Processing request of type ListResourcesRequest
[INFO] 2025-10-10T11:26:20 mcp_agent.workflows.llm.augmented_llm_openai.database-anayst - Using reasoning model 'gpt-5-mini-2025-08-07' with
'medium' reasoning effort
[INFO] 2025-10-10T11:26:23 mcp_agent.mcp.mcp_aggregator.database-anayst - Requesting tool call
{
"data": {
"progress_action": "Calling Tool",
"tool_name": "list_databases",
"server_name": "clickhouse",
"agent_name": "database-anayst"
}
}
2025-10-10 11:26:23,477 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-10-10 11:26:23,479 - mcp-clickhouse - INFO - Listing all databases
2025-10-10 11:26:23,479 - mcp-clickhouse - INFO - Creating ClickHouse client connection to sql-clickhouse.clickhouse.com:8443 as demo (secure=True, verify=True, connect_timeout=30s, send_receive_timeout=30s)
2025-10-10 11:26:24,375 - mcp-clickhouse - INFO - Successfully connected to ClickHouse server version 25.8.1.8344
2025-10-10 11:26:24,551 - mcp-clickhouse - INFO - Found 38 databases
[INFO] 2025-10-10T11:26:26 mcp_agent.mcp.mcp_aggregator.database-anayst - Requesting tool call
{
"data": {
"progress_action": "Calling Tool",
"tool_name": "list_tables",
"server_name": "clickhouse",
"agent_name": "database-anayst"
}
}
2025-10-10 11:26:26,825 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-10-10 11:26:26,832 - mcp-clickhouse - INFO - Listing tables in database 'uk'
2025-10-10 11:26:26,832 - mcp-clickhouse - INFO - Creating ClickHouse client connection to sql-clickhouse.clickhouse.com:8443 as demo (secure=True, verify=True, connect_timeout=30s, send_receive_timeout=30s)
2025-10-10 11:26:27,311 - mcp-clickhouse - INFO - Successfully connected to ClickHouse server version 25.8.1.8344
2025-10-10 11:26:28,738 - mcp-clickhouse - INFO - Found 9 tables
[INFO] 2025-10-10T11:26:48 mcp_agent.mcp.mcp_aggregator.database-anayst - Requesting tool call
{
"data": {
"progress_action": "Calling Tool",
"tool_name": "run_select_query",
"server_name": "clickhouse",
"agent_name": "database-anayst"
}
}
[INFO] 2025-10-10T11:26:48 mcp_agent.mcp.mcp_aggregator.database-anayst - Requesting tool call
{
"data": {
"progress_action": "Calling Tool",
"tool_name": "run_select_query",
"server_name": "clickhouse",
"agent_name": "database-anayst"
}
}
[INFO] 2025-10-10T11:26:48 mcp_agent.mcp.mcp_aggregator.database-anayst - Requesting tool call
{
"data": {
"progress_action": "Calling Tool",
"tool_name": "run_select_query",
"server_name": "clickhouse",
"agent_name": "database-anayst"
}
}
[INFO] 2025-10-10T11:26:48 mcp_agent.mcp.mcp_aggregator.database-anayst - Requesting tool call
{
"data": {
"progress_action": "Calling Tool",
"tool_name": "run_select_query",
"server_name": "clickhouse",
"agent_name": "database-anayst"
}
}
[INFO] 2025-10-10T11:26:48 mcp_agent.mcp.mcp_aggregator.database-anayst - Requesting tool call
{
"data": {
"progress_action": "Calling Tool",
"tool_name": "run_select_query",
"server_name": "clickhouse",
"agent_name": "database-anayst"
}
}
2025-10-10 11:26:48,366 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-10-10 11:26:48,367 - mcp-clickhouse - INFO - Executing SELECT query: SELECT
count(*) AS transactions,
avg(price) AS avg_price,
quantileExact(0.5)(price) AS median_price,
min(price) AS min_price,
max(price) AS max_price
FROM uk.uk_price_paid_simple_partitioned
WHERE toYear(date)=2025
2025-10-10 11:26:48,367 - mcp-clickhouse - INFO - Creating ClickHouse client connection to sql-clickhouse.clickhouse.com:8443 as demo (secure=True, verify=True, connect_timeout=30s, send_receive_timeout=30s)
2025-10-10 11:26:49,262 - mcp-clickhouse - INFO - Successfully connected to ClickHouse server version 25.8.1.8344
2025-10-10 11:26:49,407 - mcp-clickhouse - INFO - Query returned 1 rows
2025-10-10 11:26:49,408 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-10-10 11:26:49,408 - mcp-clickhouse - INFO - Executing SELECT query: SELECT toMonth(date) AS month, count(*) AS transactions, avg(price) AS avg_price, quantileExact(0.5)(price) AS median_price
FROM uk.uk_price_paid_simple_partitioned
WHERE toYear(date)=2025
GROUP BY month
ORDER BY month
2025-10-10 11:26:49,408 - mcp-clickhouse - INFO - Creating ClickHouse client connection to sql-clickhouse.clickhouse.com:8443 as demo (secure=True, verify=True, connect_timeout=30s, send_receive_timeout=30s)
2025-10-10 11:26:49,857 - mcp-clickhouse - INFO - Successfully connected to ClickHouse server version 25.8.1.8344
2025-10-10 11:26:50,067 - mcp-clickhouse - INFO - Query returned 8 rows
2025-10-10 11:26:50,068 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-10-10 11:26:50,069 - mcp-clickhouse - INFO - Executing SELECT query: SELECT town, count(*) AS transactions, avg(price) AS avg_price
FROM uk.uk_price_paid_simple_partitioned
WHERE toYear(date)=2025
GROUP BY town
HAVING transactions >= 50
ORDER BY avg_price DESC
LIMIT 10
2025-10-10 11:26:50,069 - mcp-clickhouse - INFO - Creating ClickHouse client connection to sql-clickhouse.clickhouse.com:8443 as demo (secure=True, verify=True, connect_timeout=30s, send_receive_timeout=30s)
2025-10-10 11:26:50,594 - mcp-clickhouse - INFO - Successfully connected to ClickHouse server version 25.8.1.8344
2025-10-10 11:26:50,741 - mcp-clickhouse - INFO - Query returned 10 rows
2025-10-10 11:26:50,744 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-10-10 11:26:50,746 - mcp-clickhouse - INFO - Executing SELECT query: SELECT toYear(date) AS year, count(*) AS transactions, avg(price) AS avg_price, quantileExact(0.5)(price) AS median_price
FROM uk.uk_price_paid_simple_partitioned
WHERE toYear(date) IN (2024,2025)
GROUP BY year
ORDER BY year
2025-10-10 11:26:50,747 - mcp-clickhouse - INFO - Creating ClickHouse client connection to sql-clickhouse.clickhouse.com:8443 as demo (secure=True, verify=True, connect_timeout=30s, send_receive_timeout=30s)
2025-10-10 11:26:51,256 - mcp-clickhouse - INFO - Successfully connected to ClickHouse server version 25.8.1.8344
2025-10-10 11:26:51,447 - mcp-clickhouse - INFO - Query returned 2 rows
2025-10-10 11:26:51,449 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-10-10 11:26:51,452 - mcp-clickhouse - INFO - Executing SELECT query: SELECT type, count(*) AS transactions, avg(price) AS avg_price, quantileExact(0.5)(price) AS median_price
FROM uk.uk_price_paid
WHERE toYear(date)=2025
GROUP BY type
ORDER BY avg_price DESC
2025-10-10 11:26:51,452 - mcp-clickhouse - INFO - Creating ClickHouse client connection to sql-clickhouse.clickhouse.com:8443 as demo (secure=True, verify=True, connect_timeout=30s, send_receive_timeout=30s)
2025-10-10 11:26:51,952 - mcp-clickhouse - INFO - Successfully connected to ClickHouse server version 25.8.1.8344
2025-10-10 11:26:52,166 - mcp-clickhouse - INFO - Query returned 5 rows
[INFO] 2025-10-10T11:27:51 mcp_agent.mcp_basic_agent - Summary (TL;DR)
- Based on the UK Price Paid tables in ClickHouse, for transactions recorded in 2025 so far there are 376,633 sales with an average price of
£362,283 and a median price of £281,000. The data appears to include only months Jan–Aug 2025 (so 2025 is incomplete). There are extreme
outliers (min £100, max £127,700,000) that skew the mean.
What I computed (how)
I ran aggregations on the uk.price-paid tables in ClickHouse:
- overall 2025 summary (count, mean, median, min, max) from uk.uk_price_paid_simple_partitioned
- monthly breakdown for 2025 (transactions, mean, median)
- top towns in 2025 by average price (towns with >= 50 transactions)
- year comparison: 2024 vs 2025 (count, mean, median)
- breakdown by property type for 2025 (counts, avg, median) using uk.uk_price_paid
Key numbers (from the dataset)
- Overall 2025 (recorded transactions): transactions = 376,633; mean price = £362,282.66; median price = £281,000; min = £100; max =
£127,700,000.
- By month (2025): (month, transactions, mean price, median price)
- Jan: 53,927, mean £386,053, median £285,000
- Feb: 58,740, mean £371,803, median £285,000
- Mar: 95,274, mean £377,200, median £315,000
- Apr: 24,987, mean £331,692, median £235,000
- May: 39,013, mean £342,380, median £255,000
- Jun: 41,446, mean £334,667, median £268,500
- Jul: 44,431, mean £348,293, median £277,500
- Aug: 18,815, mean £364,653, median £292,999
(Only months 1–8 are present in the dataset.)
- Top towns by average price (2025, towns with ≥50 transactions)
- TRING: 126 txns, avg £1,973,274
- BUCKHURST HILL: 98 txns, avg £1,441,331
- ASCOT: 175 txns, avg £1,300,748
- RADLETT: 69 txns, avg £1,160,217
- COBHAM: 115 txns, avg £1,035,192
- EAST MOLESEY, BEACONSFIELD, ESHER, CHALFONT ST GILES, THAMES DITTON are also in the top 10 (all high-average commuter/affluent towns).
- Year comparison (2024 vs 2025 as recorded)
- 2024: 859,960 transactions, mean £390,879, median £280,000
- 2025: 376,633 transactions, mean £362,283, median £281,000
(2025 counts are much lower because the dataset only includes part of the year.)
- By property type (2025)
- detached: 85,362 txns, avg £495,714, median £415,000
- semi-detached: 107,580 txns, avg £319,922, median £270,000
- flat: 62,975 txns, avg £298,529, median £227,000
- terraced: 112,832 txns, avg £286,616, median £227,000
- other: 7,884 txns, avg £1,087,765 (median £315,000) — note small-group and outlier effect
Important caveats and data quality notes
- The dataset appears partial for 2025 (only months Jan–Aug present). Any “2025” totals are not full-year figures.
- Large outliers exist (e.g., max £127.7M, and min £100). These likely include data-entry errors or non-standard records and inflate the
mean. Median is often a more robust measure here.
- “other” property-type averages are unstable due to low/heterogeneous counts and outliers.
- I did not filter by is_new, duration, or other metadata; those filters can change results (for example excluding new-builds or
leaseholds).
- The tables are Price Paid-style transaction records (recorded sales) — they do not directly represent asking prices or valuations.
Suggested next steps (I can run these)
- Clean out obvious outliers (e.g., prices < £10k or > £10M) and recompute averages/medians.
- Produce regional / county / postcode-area summaries and maps.
- Compute month-on-month or rolling 3-month median to show trend through 2025.
- Produce year-on-year (YoY) growth rates by month (e.g., Mar 2025 vs Mar 2024).
- Forecast for full 2025 using simple extrapolation or time-series modelling (but better after deciding how to handle missing
months/outliers).
If you want, I can:
- Re-run the same aggregations after removing extreme outliers and show cleaned results.
- Produce YoY monthly growth and charts (I can return CSV or JSON aggregates you can chart).
Which would you like me to do next?
[INFO] 2025-10-10T11:27:51 mcp_agent.mcp.mcp_aggregator.database-anayst - Last aggregator closing, shutting down all persistent
connections...
[INFO] 2025-10-10T11:27:51 mcp_agent.mcp.mcp_connection_manager - Disconnecting all persistent server connections...
[INFO] 2025-10-10T11:27:51 mcp_agent.mcp.mcp_connection_manager - clickhouse: Requesting shutdown...
[INFO] 2025-10-10T11:27:51 mcp_agent.mcp.mcp_connection_manager - All persistent server connections signaled to disconnect.
[INFO] 2025-10-10T11:27:52 mcp_agent.mcp.mcp_aggregator.database-anayst - Connection manager successfully closed and removed from context
[INFO] 2025-10-10T11:27:52 mcp_agent.mcp_basic_agent - MCPApp cleanup
{
"data": {
"progress_action": "Finished",
"target": "mcp_basic_agent",
"agent_name": "mcp_application_loop"
}
}