python-devtools
Enables live runtime inspection of any Python application, allowing MCP clients to query state, evaluate expressions, inspect objects, and read source code while the app runs.
README
<div align="center">
python-devtools
Live runtime inspection for any Python app — MCP-powered.
Connect Claude Code (or any MCP client) to your running Python process.<br> Query state, eval expressions, inspect objects, read source — all while the app runs.
<br>
┌─────────────────┐ TCP/JSON-lines ┌──────────────────┐
│ │ ◄──────────────────────────────►│ │
│ Your App │ localhost:auto │ MCP Bridge │
│ (3 lines) │ │ (stdio ↔ TCP) │
│ │ │ │
└─────────────────┘ └────────┬─────────┘
│ MCP stdio
┌────────▼─────────┐
│ Claude Code │
│ or any MCP │
│ client │
└──────────────────┘
<br>
Install · Quick Start · Wrapper Mode · Tools · Threading · Security
</div>
Install
# Install once (includes MCP bridge dependency)
pip install python-devtools
Or with uv:
uv add python-devtools
Or run the MCP bridge through uv:
uv run --project /abs/project/path --with mcp python-devtools
Quick Start
1. Embed in your app
import python_devtools as devtools
devtools.register('app', my_app)
devtools.register('db', database)
devtools.start(app_id='my-app') # localhost:<auto free port>
Three lines. Your app now speaks devtools.
2. Connect your agent
The bridge connects to already-running apps only — it never launches your program.
Claude Code — install as a plugin (preferred, MCP applies globally once enabled):
/plugin marketplace add holo-q/python-devtools-mcp
/plugin install python-devtools@python-devtools
Or the manual route — add to your .claude/settings.json:
{
"mcpServers": {
"python-devtools": {
"command": "python-devtools"
}
}
}
Codex CLI — one-shot global registration:
codex mcp add python-devtools -- python-devtools
This writes the [mcp_servers.python-devtools] block into ~/.codex/config.toml. Alternatively, the repo ships a .codex/config.toml for project-scoped use (active once you trust the project).
If you prefer uv-managed execution and want to guarantee the MCP SDK is present:
{
"mcpServers": {
"python-devtools": {
"type": "stdio",
"command": "uv",
"args": ["run", "--project", "/abs/project/path", "--with", "mcp", "python-devtools", "--app-id", "my-app"]
}
}
}
3. Inspect live state
Claude can now reach into your running app:
> run("len(app.users)", app_id="my-app")
→ 42
> inspect("app.config", app_id="my-app")
→ {type: AppConfig, attrs: [{name: debug, type: bool, repr: True}, ...]}
> run("app.users[0].email", app_id="my-app")
→ alice@example.com
> source("type(app.users[0]).validate", app_id="my-app")
→ def validate(self): ...
Discovery (No Fixed Ports)
devtools.start() defaults to port=0, so each app instance binds an available free port.
- App side: each running instance writes
{app_id, host, port, pid}to a local registry - Bridge side: tools resolve
app_idto the current endpoint from that registry - Unknown
app_id: the bridge pings candidates and returns the list of running apps - Crash/system-crash safety: stale registry records are pruned automatically when liveness checks fail
This removes the need to reserve one static port per app.
Wrapper Mode
Don't want to modify your app's source? Wrap it:
python-devtools --app-id myapp -- uv run myapp.py
python-devtools --app-id flask-dev -- flask run
python-devtools --app-id worker --port 9230 -- python worker.py
python-devtools --readonly -- python myapp.py # locks down mutation tools
If --app-id is omitted, the wrapper synthesizes one from the entry script and pid (e.g. myapp-12345) so multiple instances of the same script don't collide in the registry.
This injects a devtools server into the child process via sitecustomize.py — no code changes needed. The child gets a TCP server on startup, and __main__ is auto-registered as main:
> run("dir(main)")
→ ['__builtins__', '__file__', 'app', 'config', 'db', ...]
> run("main.app.config['DEBUG']")
→ True
<details> <summary><b>How it works</b></summary> <br>
The wrapper prepends a generated sitecustomize.py to PYTHONPATH. When the child Python interpreter starts, site.py imports it, which:
- Chains to any existing
sitecustomize.py(removes inject dir from path, imports original, restores) - Starts the devtools TCP server on a free port (or your configured port)
- Registers
__main__— the module ref is captured early but populated later with the script's globals
The python_devtools package is also added to PYTHONPATH, so it doesn't need to be installed in the child's environment.
Non-Python children (e.g., python-devtools -- node app.js) are harmless — the env vars are set but nothing reads them.
</details>
<br>
Pair with the MCP bridge for Claude Code access:
# Terminal 1: run your app with devtools injected
python-devtools --app-id myapp -- uv run myapp.py
# Claude Code config: MCP bridge routes by app_id
# .claude/settings.json
{
"mcpServers": {
"python-devtools": {
"command": "python-devtools"
}
}
}
Tools
<table> <tr> <th width="160">Tool</th> <th>Description</th> <th width="80">Mutates</th> </tr> <tr> <td><code>running_apps</code></td> <td>List reachable app IDs discovered from the local registry (stale entries are auto-pruned)</td> <td align="center">—</td> </tr> <tr> <td><code>run</code></td> <td>Eval an expression or exec a statement in the app's live namespace</td> <td align="center">yes</td> </tr> <tr> <td><code>call</code></td> <td>Call a callable at a dotted path with args/kwargs</td> <td align="center">yes</td> </tr> <tr> <td><code>set_value</code></td> <td>Set an attribute or item at a dotted path</td> <td align="center">yes</td> </tr> <tr> <td><code>inspect</code></td> <td>Structured inspection — type, repr, public attrs, recursive</td> <td align="center">—</td> </tr> <tr> <td><code>list_path</code></td> <td>Shallow enumeration — attrs, keys, or items at a path</td> <td align="center">—</td> </tr> <tr> <td><code>repr_obj</code></td> <td>Quick type + repr — fastest tool, minimal overhead</td> <td align="center">—</td> </tr> <tr> <td><code>source</code></td> <td>Get source code of a function, class, or method</td> <td align="center">—</td> </tr> <tr> <td><code>state</code></td> <td>List all registered namespaces and their types</td> <td align="center">—</td> </tr> <tr> <td><code>logs</code></td> <td>Indexed log tail (default), paging (<code>before_id</code>), and follow mode (<code>after_id</code> + <code>wait_seconds</code>)</td> <td align="center">—</td> </tr> <tr> <td><code>screenshot</code></td> <td>Capture the live app's GUI as a PNG (requires <code>set_screenshot_fn</code>)</td> <td align="center">—</td> </tr> <tr> <td><code>winshot</code></td> <td>Render a code snippet in an isolated offscreen window and return a PNG (requires <code>set_winshot_fn</code>)</td> <td align="center">yes</td> </tr> <tr> <td><code>ping</code></td> <td>Connection health check (returns running apps when <code>app_id</code> is omitted)</td> <td align="center">—</td> </tr> </table>
Every tool accepts an optional <code>app_id</code> argument. If no default app ID is set on the bridge and the supplied app ID is not found, the bridge pings known endpoints, prunes stale records, and returns the running apps list.
run also accepts optional <code>max_result_chars</code> and <code>max_result_lines</code> limits (defaults: 0 / 0, meaning no truncation). Set either value to > 0 to compact very large text outputs for model readability — the response carries a head/tail preview and a top_patterns summary so models can still reason about long log spew.
Mutation tools (run, call, set_value, winshot) attach a devtools_warning field if the target app's pid/port changes immediately after the call — surfacing crashes or restarts that would otherwise look like silent successes.
Debugging Pattern (Timeline First)
For agent-assisted debugging, prefer logs over brute-force state probing:
- Take a tail snapshot:
logs(limit=200, app_id="my-app") - Reproduce the issue and follow new lines:
logs(after_id=<last_id>, wait_seconds=5, app_id="my-app") - Page older context when needed:
logs(before_id=<first_id>, limit=200, app_id="my-app")
Log results include stable id indices so agents can move backward/forward deterministically.
Argparse Integration
For apps that already use argparse:
import argparse
import python_devtools as devtools
parser = argparse.ArgumentParser()
devtools.add_arguments(parser) # adds --devtools, --devtools-port, --devtools-app-id, --devtools-readonly
args = parser.parse_args()
devtools.from_args(args, app=my_app, db=database)
python myapp.py --devtools --devtools-app-id myapp
python myapp.py --devtools --devtools-app-id myapp --devtools-port 9230
python myapp.py --devtools --devtools-app-id myapp --devtools-readonly
Threading Safety
GUI apps, game loops, and anything with a main-thread constraint need an invoker:
import concurrent.futures
import queue
main_queue = queue.Queue()
def invoke_on_main(fn):
"""Route devtools calls onto the main thread."""
future = concurrent.futures.Future()
main_queue.put((fn, future))
return future.result(timeout=10)
devtools.set_main_thread_invoker(invoke_on_main)
devtools.start()
# In your main loop:
while running:
while not main_queue.empty():
fn, future = main_queue.get()
future.set_result(fn())
# ... rest of frame
Without an invoker, calls run inline on the TCP handler thread (a one-time warning is emitted).
GUI Capture (screenshot / winshot)
Two optional callbacks expose visual state to the agent. Both run through the main-thread invoker, so they have access to the app's framebuffer / GL context.
def capture() -> bytes:
"""Capture current visual state as PNG bytes."""
...
def render_winshot(code: str) -> bytes:
"""Render `code` into an isolated offscreen window, return PNG bytes."""
...
devtools.set_screenshot_fn(capture) # enables the `screenshot` MCP tool
devtools.set_winshot_fn(render_winshot) # enables the `winshot` MCP tool
screenshotcaptures the whole live app — useful for "what does the user actually see right now".winshotcaptures only the code the agent passes (a single panel, a test widget, a component in isolation) — useful for verifying UI changes without spinning up the full app state.
Both tools cleanly error out with an actionable message if the app hasn't registered the corresponding callback. The bridge converts the returned PNG bytes into an Image MCP payload so Claude Code renders it inline.
Readonly Mode
Lock down mutation tools for safer inspection:
devtools.start(readonly=True)
{
"mcpServers": {
"python-devtools": {
"command": "python-devtools",
"args": ["--readonly"]
}
}
}
In readonly mode, run, call, set_value, and winshot are not registered — only inspection and screenshot are available. The server also enforces this at the protocol layer, so a stale read-write bridge against a readonly app still gets rejected.
Plugin / Extension Layout
This repo doubles as a Claude Code plugin and a Codex CLI project-scoped extension. The relevant files:
.claude-plugin/
├── plugin.json # Claude Code plugin manifest
└── marketplace.json # Lets the repo serve itself as a single-plugin marketplace
.mcp.json # MCP server registration consumed by Claude Code on plugin enable
.codex/
└── config.toml # Codex CLI project-scoped MCP registration (loads on trust)
AGENTS.md # Auto-loaded by Codex; agent-facing usage doc for python-devtools
Claude Code install flow — plugin enable wires the MCP server globally (every Claude Code session, not just inside this repo):
/plugin marketplace add holo-q/python-devtools-mcp
/plugin install python-devtools@python-devtools
Codex CLI install flow — Codex has no plugin system, only config files. Cleanest path:
codex mcp add python-devtools -- python-devtools # writes ~/.codex/config.toml globally
For project scope only, run codex inside this repo and accept the trust prompt — .codex/config.toml is then honored automatically.
Security
<div align="center">
LOCAL_TRUSTED — loopback only, no auth, eval enabled.
</div>
This is a development tool, not a production service.
- Binds to localhost only — non-loopback connections are rejected
- No authentication — anyone on localhost can connect
- eval/exec is unrestricted — full access to your Python process
- The
readonlyflag disables mutation tools but does not add auth
Do not expose to networks. Do not run in production.
Observable State
For GUI status indicators, the server exposes:
devtools.running # bool — is the server listening?
devtools.n_clients # int — currently connected clients
devtools.n_commands # int — total commands processed
devtools.last_command_time # float — time.time() of last command
Architecture
python-devtools/
├── __init__.py # Module API — register, start, stop, set_*_fn, add_arguments, from_args
├── _core.py # DevTools orchestrator — lifecycle, argparse, callback registration
├── _registry.py # Local app registry (XDG cache) — app_id → host/port/pid lookup
├── _server.py # TCP JSON-lines server — accept loop, dispatch, log capture, loopback guard
├── _resolve.py # Object resolution — eval/exec, inspect, serialize, compaction
├── _cli.py # MCP stdio bridge — app-id router, mutation pid/port watchdog
└── _wrap.py # Wrapper mode — sitecustomize.py injection via PYTHONPATH
The app runtime server (__init__, _core, _server, _resolve, _registry) is pure stdlib — zero deps in your app's process.
The MCP bridge (_cli) uses the bundled mcp dependency, which ships in the base install (no extras to remember).
Wire-level summary
agent ──► MCP stdio ──► _cli (bridge) ──► TCP JSON-lines ──► _server ──► _resolve ──► your objects
│ │
└─ registry (~/.cache/python-devtools/registry/*.json) ──┘
Each devtools.start() writes {app_id, host, port, pid, readonly} to the registry; the bridge resolves the agent's app_id to the freshest live entry, prunes dead ones via ping, and reuses TCP clients per endpoint.
<div align="center"> <sub>MIT License</sub> </div>
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。