cab938

[2025-12-09][19:46:10][app_lib::core::mcp::helpers][INFO] Starting MCP server Jan Browser MCP (Initial attempt)
[2025-12-09][19:46:10][app_lib::core::mcp::helpers][WARN] Port 17389 occupied, attempting cleanup
[2025-12-09][19:46:10][app_lib::core::mcp::helpers][DEBUG] Found lock file for port 17389: PID=3752159
[2025-12-09][19:46:10][app_lib::core::mcp::helpers][WARN] Lock file PID 3752159 is alive but NOT an MCP process (name: Jan, cmd: ["Jan"]). Lock file is stale.
[2025-12-09][19:46:10][app_lib::core::mcp::lockfile][DEBUG] Found lock file for port 17389: PID=3752159, server=Jan Browser MCP
[2025-12-09][19:46:10][app_lib::core::mcp::lockfile][DEBUG] Process 3752159 is still alive for port 17389
[2025-12-09][19:46:10][app_lib::core::mcp::helpers][INFO] Found process on port 17389: PID=1020738, name=node, cmd=["node", "/tmp/bunx-1000-search-mcp-server@latest/node_modules/.bin/search-mcp-server"]
[2025-12-09][19:46:10][app_lib::core::mcp::helpers][INFO] Killing orphaned MCP process: PID 1020738
[2025-12-09][19

cab938

#!/usr/bin/env python3
"""
Transparent stdio wrapper for MCP processes.

This helper launches a child process, forwards stdin, stdout, and stderr between
the parent and child, and tees every byte into timestamped log files. The wrapper
avoids writing to stdout so that MCP JSON exchanges remain intact. Optional
verbose logs go to stderr for troubleshooting.

Examples

cab938

absl-py==2.2.2
aiofiles==24.1.0
aiohappyeyeballs==2.6.1
aiohttp==3.11.18
aiosignal==1.3.2
alembic==1.15.2
altair==5.5.0
annotated-types==0.7.0
anyio==4.9.0
argon2-cffi==23.1.0

cab938

aiofiles==23.2.1
aiohappyeyeballs==2.4.3
aiohttp==3.10.10
aiosignal==1.3.1
altair==5.4.1
annotated-types==0.7.0
anyio==4.6.0
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0

cab938

146.204.224.152 - feest6811 [21/Jun/2019:15:45:24 -0700] "POST /incentivize HTTP/1.1" 302 4622
197.109.77.178 - kertzmann3129 [21/Jun/2019:15:45:25 -0700] "DELETE /virtual/solutions/target/web+services HTTP/2.0" 203 26554
156.127.178.177 - okuneva5222 [21/Jun/2019:15:45:27 -0700] "DELETE /interactive/transparent/niches/revolutionize HTTP/1.1" 416 14701
100.32.205.59 - ortiz8891 [21/Jun/2019:15:45:28 -0700] "PATCH /architectures HTTP/1.0" 204 6048
168.95.156.240 - stark2413 [21/Jun/2019:15:45:31 -0700] "GET /engage HTTP/2.0" 201 9645
71.172.239.195 - dooley1853 [21/Jun/2019:15:45:32 -0700] "PUT /cutting-edge HTTP/2.0" 406 24498
180.95.121.94 - mohr6893 [21/Jun/2019:15:45:34 -0700] "PATCH /extensible/reinvent HTTP/1.1" 201 27330
144.23.247.108 - auer7552 [21/Jun/2019:15:45:35 -0700] "POST /extensible/infrastructures/one-to-one/enterprise HTTP/1.1" 100 22921
2.179.103.97 - lind8584 [21/Jun/2019:15:45:36 -0700] "POST /grow/front-end/e-commerce/robust HTTP/2.0" 304 14641
241.114.184.133 - tromp8355 [21/Jun/2019:15

cab938

Ronald Mayr: A
Bell Kassulke: B
Jacqueline Rupp: A 
Alexander Zeller: C
Valentina Denk: C 
Simon Loidl: B 
Elias Jovanovic: B 
Stefanie Weninger: A 
Fabian Peer: C 
Hakim Botros: B

cab938

#!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/asia/land/ytd/12/1910-2016', header=0)[0]
pop1=df[df['Year']<1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)
pop2=df[df['Year']>=1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))

cab938

!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/asia/land/ytd/12/1910-2016', header=0)[0]
pop1 = #put the cleaned list of all temperature anomalies for pre 1950
pop2 = #put the cleaned list of all temperature anomalies for 1950 and above

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))

cab938

#!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/northAmerica/land/ytd/12/1880-2016', header=0)[0]
pop1=df[df['Year']<1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)
pop2=df[df['Year']>=1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))

cab938

!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/northAmerica/land/ytd/12/1880-2016', header=0)[0]
pop1 = #put the cleaned list of all temperature anomalies for pre 1950
pop2 = #put the cleaned list of all temperature anomalies for 1950 and above

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))