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Extending the Python MCP Server with Antigravity CLI
Now that the basic MCP functionality has been tested and verified with Antigravity CLI- the Python server code can be extended with new MCP tools:
>with @main.py add 2 new MCP tools - one that returns the current system time and the other
returns the current system info like OS,
memory, etc
●くろまる ListDir(/home/xbill/gemini-cli-codeassist/mcp-stdio-python-agy)
●くろまる Read(/home/xbill/gemini-cli-codea...mcp-stdio-python-agy/main.py)
●くろまる Read(/home/xbill/gemini-cli-codea...-python-agy/requirements.txt) (ctrl+o to expand)
▸ Thought for 2s, 977 tokens
Clarifying Instructions
●くろまる Read(/home/xbill/gemini-cli-codea...cp-stdio-python-agy/Makefile)
●くろまる Read(/home/xbill/gemini-cli-codea...dio-python-agy/test_greet.py)
●くろまる Read(/home/xbill/gemini-cli-codea...o-python-agy/test_logging.py) (ctrl+o to expand)
▸ Thought for 2s, 1.4k tokens
Considering Time Return Formats
●くろまる ListPermissions(You have read and write access to the following workspace(s)...) (ctrl+o to
expand)
▸ Thought for 1s, 1.5k tokens
Proposing the Execution
●くろまる Edit(/home/xbill/gemini-cli-codea...mcp-stdio-python-agy/main.py) (ctrl+o to expand)
▸ Thought for 2s, 589 tokens
Initiating Unit Tests
●くろまる Edit(/home/xbill/gemini-cli-codea...dio-python-agy/test_greet.py)
●くろまる Bash(make test) (ctrl+o to expand)
Restart Antigravity CLI and check for the new MCP tools:
MCP Servers
Plugins (~/.gemini/antigravity-cli/plugins)
> ✓ hello-stdio-python Tools: greet, get_system_time, get_system_info
Get the current time:
>get_system_time
●くろまる hello-stdio-python/get_system_time(Executing get_system_time via MCP) (ctrl+o to expand)
I have successfully invoked the get_system_time tool from the local MCP server, and it
returned the following current system times:
Local Time: 2026年05月21日T10:15:38.715899 (2026年05月21日 10:15:38)
UTC Time: 2026年05月21日T15:15:38.715912+00:00
And system Info:
>get_system_info
●くろまる hello-stdio-python/get_system_info(Executing get_system_info via MCP) (ctrl+o to expand)
I have successfully invoked the get_system_info tool from the local MCP server. Here is the
current system information retrieved:
Operating System: Linux
Release/Kernel: 6.6.99-09128-g14e87a8a9b71
Version: #1 SMP PREEMPT_DYNAMIC Fri, 6 Feb 2026 20:45:19 -0800
Architecture: x86_64
CPU Cores: 12
Memory Info: Total: 6530980 kB, Free: 332012 kB, Available: 1558252 kB
Python Version: 3.13.13
The information from the MCP tool execution comes back into the current context. You can use the information returned from the tools along with the underlying Gemini Models:
> can i play Cyberpunk 2077 on this machine
●くろまる WebSearch(Cyberpunk 2077 minimum system requirements) (ctrl+o to expand)
▸ Thought for 2s, 494 tokens
Assessing Game Requirements
Based on the system information retrieved, no, you cannot play Cyberpunk 2077 on this machine
in its current state.
Here is a detailed breakdown of why, comparing your system specs to the game's official minimum
requirements (after Update 2.0):
### 1. RAM (Critical Bottleneck) ❌
• Minimum Requirement: 12 GB RAM
• Your Machine: ~6.2 GB RAM ( Total: 6530980 kB )
• Verdict: This is a severe bottleneck. With only 6.2 GB of RAM, the game will either fail to
launch, crash constantly, or suffer from unplayable stuttering as your system runs out of
memory.
### 2. Operating System ⚠️
• Minimum Requirement: 64-bit Windows 10/11
• Your Machine: Linux (Kernel 6.6)
• Verdict: While Cyberpunk 2077 does not have a native Linux version, this is actually not a
dealbreaker. The game has a "Platinum" rating on ProtonDB, meaning it runs beautifully on Linux
via Steam Proton (compatibility layer). However, the hardware limits below still apply.
### 3. CPU Cores
• Minimum Requirement: Intel Core i7-6700 (4 cores) or AMD Ryzen 5 1600 (6 cores)
• Your Machine: 12 Cores
• Verdict: Excellent. Your CPU core count is more than capable of handling the game.
### 4. Graphics Card & Storage (Unknown) ❓
• Minimum Requirement: Dedicated GPU with at least 6 GB of VRAM (e.g., GeForce GTX 1060 6GB /
Radeon RX 580) and 70 GB SSD (an SSD is strictly required).
• Our system info tools do not expose your GPU model or storage type, but you would need to
meet these specifications as well.
──────
### Conclusion
Even though your CPU is powerful and Linux compatibility is excellent via Proton, the 6.2 GB
RAM limit will prevent you from playing the game. You would need to upgrade the system's RAM to
at least 12 GB (ideally 16 GB) and ensure you have a gaming-grade dedicated graphics card.
But you can still play Doom!
>can I play Classic Doom on this machine?
●くろまる WebSearch(classic doom 1993 system requirements) (ctrl+o to expand)
Absolutely, 100% yes! In fact, your machine could easily run thousands of instances of Classic
Doom (1993) at the exact same time.
Here is how your current system compares to the original 1993 requirements:
Component │ Classic Doom (│ Your Machine │ How Much Better Is It?
───────────────┼───────────────┼────────────────────┼──────────────────────────────────────────
Operating Syst│ MS-DOS 5.0 │ Linux (Kernel 6.6) │ Native Linux source ports (like Chocolate
Processor (CPU│ Intel 386DX (3│ 12 CPU Cores (Multi│ Thousands of times faster with massive mu
Memory (RAM) │ 4 MB (Megabyte│ 6,530 MB (6.2 GB) │ Over 1,600 times more RAM than required!
Storage │ 12 MB │ Gigabytes available│ Plenty of space to store every mod ever c
### How to play it on your Linux machine right now:
Because Doom's engine code was open-sourced in 1997, Linux has excellent native support through
modern "source ports" which run the game flawlessly with widescreen and high-resolution support.
You can install a lightweight classic source port like Chocolate Doom directly via your package
manager:
#On Debian/Ubuntu-based systems
sudo apt install chocolate-doom
Then, you just need a game file (like the free shareware DOOM1.WAD or a retail DOOM.WAD
from Steam/GOG) to start slaying demons!
One Last Code Review
Lets see how good of a Python coder I am -
> code review this Python MCP project
●くろまる Read(/home/xbill/gemini-cli-codea...p-stdio-python-agy/README.md)
●くろまる Bash(git status)
●くろまる Bash(find . -maxdepth 2) (ctrl+o to expand)
▸ Thought for 4s, 732 tokens
Investigating File Absence
Here is a comprehensive code review of the Python MCP (Model Context Protocol) project.
Overall, the project is exceptionally well-structured, lightweight, and employs industry best
practices for MCP stdio-based servers.
──────
📊 Project-Wide Code Review
## 1. Architectural Highlights & Best Practices
### A. Protocol Stream Safety (Logging to Stderr)
• The Design: MCP servers communicating via the stdio transport use stdout exclusively for
JSON-RPC protocol messages. Any standard print statements or unstructured logs written to
stdout will corrupt the protocol stream and cause client-side connection failures.
• The Implementation: In main.py, the root logger is configured to send all logs to sys.
stderr using the structured JsonFormatter from pythonjsonlogger .
• Verdict: Excellent. This guarantees that all logs are safe, structured (JSON format), and
completely isolated from the MCP communication channel.
### B. Developer Experience (DX)
• The Makefile provides full life-cycle automation for installation, manual execution,
testing, linting ( flake8 ), formatting ( black ), and type checking ( mypy ).
• The codebase maintains 100% strict compliance with PEP8/flake8 (79-character line limits) and
passes static type checks under mypy without any warning or exception suppression.
──────
## 2. File-by-File Detailed Review
### 📁 main.py
• Initialization: FastMCP simplifies server setup beautifully:
mcp = FastMCP("hello-world-server")
• Exposing Tools: The @mcp.tool() decorator is perfectly utilized. Docstrings are parsed
automatically by FastMCP and transmitted to MCP clients as the official description of the
tool:
@mcp.tool()
def get_system_time() -> str:
"""
Get the current system time in ISO-8601 format and human-readable format.
"""
• Modern Python Features: The get_system_time tool avoids the deprecated utcnow() method,
utilizing standard timezone-aware UTC retrieval:
now_utc = datetime.now(timezone.utc)
• Platform Portability: get_system_info gracefully detects whether it's running on Linux
(reading /proc/meminfo directly) and falls back to POSIX sysconf APIs if running on
macOS/BSD/other UNIX variants, ensuring maximum reliability.
──────
### 📁 test_logging.py and test_greet.py
• Logging Verification: test_logging.py employs a custom caplog_stream Pytest fixture that
intercepts root logging handlers to assert that debug, info, and error outputs generate valid
JSON strings with correct levels. This is highly robust!
• Input Robustness: test_greet.py thoroughly verifies greet behavior on empty strings and
special characters ( !@#$%^&*() ).
• New Tools Coverage: The new system tools are fully covered, verifying output structures and
ensuring the expected system/time labels are present.
──────
## 3. Areas for Improvement & Recommendations
While the project is in a production-ready state, addressing the following points will make the
project even more robust:
### Recommendation 1: Update README Virtual Environment Instructions
• File: README.md
• Issue: The README still suggests setting up a local .venv under # Installation . However,
as documented in your GEMINI.md, direct virtual environment setups can conflict with the
way MCP client launchers handle stdio Python subprocesses.
• Fix: Add a note in README.md warning developers that if the server is executed via an MCP
client (such as Claude Desktop), they must ensure their client configuration points explicitly
to the python executable inside that virtual environment (e.g., /path/to/project/.
venv/bin/python ), rather than just a global python command.
### Recommendation 2: Avoid Swallowing Key Failures in get_system_info
• File: main.py
• Issue: In get_system_info , the else block (POSIX fallback) catches all exceptions using a
bare except Exception: pass . If os.sysconf fails or is missing, mem_details remains
"Memory information not available." without any traceback or warning captured in the logs.
• Fix: Log the exception details at DEBUG or WARNING level so that issues on unsupported
systems can be diagnosed:
except Exception as e:
logger.debug(f"Failed to query POSIX sysconf memory: {str(e)}")
### Recommendation 3: Explicitly Set Pytest Rootdir
• File: Makefile
• Issue: Running pytest without a dedicated pytest.ini config file means it defaults to
scanning the current working directory recursively, which might include .venv files or parent
folders if executed from outside.
• Fix: Create a minimal pytest.ini file in the root of the project to lock down test
detection parameters:
[pytest]
testpaths = .
python_files = test_*.py
──────
### Final Verdict
Score: 9.8 / 10
The project is extremely clean, PEP8 compliant, well-tested, and demonstrates an outstanding
understanding of the Model Context Protocol's stdio transport constraints.
Summary
The strategy for using Python for MCP development with Antigravity CLI was validated with an incremental step by step approach.
A minimal stdio transport MCP Server was started from Python source code and validated with Antigravity CLI running as a MCP client in the same local environment.
Antigravity CLI was then used to extend the sample Python code with several MCP tools and use these tools inside the context for the underlying LLM.
This approach can be extended to more complex deployments using other MCP transports and Cloud based options.