原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Agentguard47

作者:bmdhodl版本 0.2.2i★ –i在你的电脑上运行
注册名称io.github.bmdhodl/agentguard47

安装

概览

作者提供的说明(英文原文)

Read-only MCP server for coding-agent traces, alerts, costs, usage, and budget health.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 诊所与健康
  • 你需要填写:AGENTGUARD_API_KEY

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: AGENTGUARD_API_KEY, AGENTGUARD_URL

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "agentguard47": {
      "command": "npx",
      "args": [
        "-y",
        "@agentguard47/mcp-server"
      ],
      "env": {
        "AGENTGUARD_API_KEY": "your-value",
        "AGENTGUARD_URL": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add agentguard47 --env AGENTGUARD_API_KEY=your-value --env AGENTGUARD_URL=your-value -- npx -y @agentguard47/mcp-server

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "agentguard47": {
      "command": "npx",
      "args": [
        "-y",
        "@agentguard47/mcp-server"
      ],
      "env": {
        "AGENTGUARD_API_KEY": "your-value",
        "AGENTGUARD_URL": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "agentguard47": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@agentguard47/mcp-server"
      ],
      "env": {
        "AGENTGUARD_API_KEY": "your-value",
        "AGENTGUARD_URL": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • AGENTGUARD_API_KEY 机密 必填 env
    AgentGuard read API key for querying traces, alerts, costs, usage, and savings.
  • AGENTGUARD_URL env
    Optional AgentGuard API base URL. Defaults to production.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 bmdhodl 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“诊所与健康”中的其他服务器比较i

  • 星标数多于此分类中其他 865 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 865 个服务器的 15% 更新
  • 基础检查结果等于或优于此分类中其他 865 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Agentguard47 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

你需要填写:AGENTGUARD_API_KEY

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.2.2)于5个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Mirobody, FHIR MCP Server, Google Health MCP

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Mirobody

作者:thetahealth

Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.

在你的电脑上运行★ 1,408Apache-2.05天前更新
安装

FHIR MCP Server

作者:wso2

MCP server providing seamless access to FHIR APIs for AI tools and healthcare applications

在你的电脑上运行★ 137Apache-2.08天前更新
安装

Google Health MCP

作者:davidmosiah

Privacy-first unofficial Google Health API v4 MCP server for health, sleep, activity, HR agents

在你的电脑上运行★ 79MIT4个月前更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

AgentGuard

Stop runaway agents with runtime checks in Python.

AgentGuard checks budgets, repeated tool calls, retries, and elapsed time in instrumented Python code. Guards raise exceptions so your application can stop the next operation. The base SDK has no runtime dependencies and needs no account.

Names: this repository is agent47, the PyPI package is agentguard47, and the Python import is agentguard. AgentGuard 2.0.0 requires Python 3.11 or newer. On Python 3.9 or 3.10, pin agentguard47==1.4.0. See the Python migration guide before upgrading.

Getting started

Install in a virtual environment, then run the offline checks:

python -m pip install agentguard47

agentguard doctor agentguard demo

doctor checks the installation and local trace writing. demo exercises budget, loop, and retry stops without provider keys or network access. Follow the trace path printed by the command to inspect its output. agentguard demo --feedback prints a local redacted report; nothing is sent.

For a fresh PowerShell install and a runnable raw starter, follow the published-release walkthrough.

Share a stop receipt (new in 2.0.0)

agentguard receipt agentguard_demo_traces.jsonl prints a receipt of each stop with the trace's SHA-256 drawn as a barcode. Add --format markdown to paste it into a PR or issue. The hash identifies the trace file; it is not a signature. This stop receipt records a covered guard refusal. Anthropic's official /receipts plugin summarizes past Claude Code work (30 days by default). Neither output proves prevented spend or provides a signed attestation. The agentguard receipt command stays the same.

Guard a Claude Code session (new in 2.0.0)
agentguard hook claude-code --install --write

This installs a Claude Code hook that refuses the third identical tool call in a row and a call that already failed twice. "Identical" ignores JSON key order and preserves case and whitespace in values. The optional no_progress_max rule refuses a tool after repeated empty or same-error results even when input changes; it is off by default. Refusals omit raw input and go to .agentguard/claude-code/trace.jsonl. It checks tool calls, not tokens or subscription quota. See the Claude Code hook guide.

Guard a script without editing it (new in 2.0.0)
agentguard run --budget-usd 5 agent.py

This patches the OpenAI and Anthropic clients, then runs agent.py in the same interpreter. Settings come from flags, then environment variables, then .agentguard.json. A guard stop exits 1. Every run ends with the trace path on stderr, ready for agentguard receipt. agentguard run python -m mypkg works too. The bounds are the same as patching the client yourself; see enforcement boundary.

Subprocesses start a separate interpreter and are not patched by the parent. A detached, uninstrumented child can keep running after the guarded script exits. The offline reproduction demonstrates that boundary with two simulated calls and no network access.

agentguard run prices every patched call at paid rates, including a local model behind an OpenAI-compatible URL such as Ollama. A dollar budget can then stop a model that costs nothing. For a local model, patch the client in the script with free_local_clients=[client] and a BudgetGuard token or call cap, then run the script with python; see free local clients.

Stop before a third call

Save this as budget_demo.py and run python budget_demo.py. It makes no network requests.

from agentguard import BudgetExceeded, BudgetGuard

budget = BudgetGuard(max_calls=2) completed = 0

for _ in range(3): try: budget.check() # Check before the operation.

Put your provider or tool call here.

completed += 1 budget.consume(calls=1) # Record the completed operation. except BudgetExceeded: print(f"Stopped before call {completed + 1}")

assert completed == 2

Expected output: Stopped before call 3.

Stop a reworded loop

A stuck agent often rewords each retry, so no call repeats exactly. max_no_progress counts results instead: the same tool coming back empty, or with the same error, N times in a row stops the run whatever the arguments. normalize=True canonicalizes containers while preserving case and whitespace in argument values. Both options are off by default.

from agentguard import LoopDetected, LoopGuard

loop = LoopGuard(normalize=True, max_no_progress=3)

for pattern in ["retry_count", "retryCount", "retries"]: loop.check("grep", {"pattern": pattern}) matches = [] # Put your search here. This one finds nothing. try: loop.record_result("grep", matches, tool_args={"pattern": pattern}) except LoopDetected as stop: print(stop)

Expected output: No progress: grep came back empty 3 times in a row (limit: 3). Change the approach instead of rewording the call. Functions wrapped with trace_tool feed results to the guard automatically. Any other result in between starts the count over, so pagination and edit-then-test cycles do not trip it.

Connect a provider

Install the provider's client separately. For OpenAI:

python -m pip install openai
from agentguard import BudgetGuard, JsonlFileSink, Tracer, patch_openai

budget = BudgetGuard(max_cost_usd=5.00) tracer = Tracer( service="my-agent", sink=JsonlFileSink(".agentguard/traces.jsonl"), ) patch_openai(tracer, budget_guard=budget)

Make your OpenAI chat.completions.create or responses.create calls after this setup.

Standard OpenAI clients created earlier are covered too, including resource objects obtained before the patch. Activate the patch before sending requests or saving bound methods and raw/streaming response helpers. Those saved references can retain the original callable; recreate them after activation. patch_openai_async() covers async clients, and agentguard.init() enables both. See the enforcement boundary.

The patch checks recorded usage before dispatch and records response usage afterward, including streamed calls once the final usage arrives. A response can exceed the remaining cost or token allowance. Concurrent requests do not reserve capacity. Chat Completions streams request include_usage unless the caller already set it.

The OpenAI Agents SDK runs on responses.create, so agentguard.init() before the Runner puts every model call under the budget (example). Hosted tools and background=True responses are not covered. See the getting started guide for setup, traces, and framework starters.

How enforcement works

flowchart TD

accTitle: AgentGuard operation checks accDescr: Check a limit before an operation, then record usage. A[Instrumented operation] --> B{Guard check} B -->|Limit reached| C[Raise exception] B -->|Allowed| D[Run operation] D --> E[Record usage and trace] E --> A

Text equivalent: check before an operation, run it if allowed, then record usage. A guard exception returns control to your application's error handler.

GuardChecksRaises
BudgetGuardRecorded calls, tokens, or estimated costBudgetExceeded
LoopGuardRepeated tool calls; opt in to normalize=True and max_no_progress=N for reworded onesLoopDetected
FuzzyLoopGuardTool frequency and alternating patternsLoopDetected
RetryGuardRetries per toolRetryLimitExceeded
TimeoutGuardElapsed time when checkedTimeoutExceeded
RateLimitGuardCalls within a sliding minuteBudgetExceeded
X402SpendGuardPayment amounts before the payment callbackBudgetExceeded

For task budgets, use BudgetGuard.goal(...). For signatures and defaults, read the guard source and public exports.

Budgets can also persist across runs: configure JsonFileStateStore, a shared key, and period="day" for a daily counter. Set warn_at_pct=0.8 and an on_warning callback for an 80% warning. Workers must explicitly share the store and key; child-agent budget inheritance is not automatic. Monthly SDK limits are not implemented. See the capability matrix.

Limits and security

  • Guards cover operations you instrument. Installing the package does not intercept every action in Cursor, Claude Code, or another agent.
  • A guard is not a sandbox or permission system. A permitted operation can still be destructive.
  • Timeout checks do not interrupt an already blocked function or cancel an agent running on a provider's server.
  • Cost estimates are not invoices. Supply reported cost or use strict cost resolution when an estimate is insufficient.
  • Recorded-budget preflight refuses the next instrumented call when stored usage is already at a cap. It does not reserve concurrent in-flight requests, predict the next response, or cap a provider subscription. See the enforcement boundary.
  • The base SDK uses the standard library. Optional framework extras install third-party dependencies and need their own security review.
  • The optional [crewai] extra pulls ChromaDB. The 2026-09-12 audit found four unresolved advisories, including PYSEC-2026-311 / CVE-2026-45829. Review that exposure before installing the extra. Base SDK installs do not include ChromaDB.
  • Trace content can contain application data. Review it before sharing or configuring a remote sink.

See security reporting, the dated dependency audit, and release notes. Audit results describe their recorded date, not a permanent clean bill of health.

Local traces and optional hosted ingest

The SDK is the free local proof path. Start local. Add hosted ingest only when you need retained history, alerts, team visibility, spend trends, hosted decision history, or dashboard-managed remote kill signals.

Local guards remain authoritative. HttpSink mirrors trace and decision events; it does not execute remote kill signals by itself. See the dashboard contract before configuring it.

Local use has no hosted event quota, retention period, or API-key allocation. Network egress requires an integration you configure, such as HttpSink or an OpenTelemetry exporter.

Nothing in the local SDK phones home. The AgentGuard website describes the optional hosted service.

Documentation

You want toStart here
See which paths actually stop a callEnforcement boundary
Install and trace a first runGetting started

| Find guides and source references | [Do

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