GitDiagram
Architecture diagrams, explanations and Mermaid source for public GitHub repositories.
MCP observability: analyse MCP server usage, investigate sessions, and instrument MCP servers.
选择你使用的 AI 应用,然后复制设置。
通过 2/4 项检查 · 你的提问会经过开发者的服务器(api.flowlines.ai) · ✓ 命令已检查 2026年11月13日
第一步:你用哪个 AI?
选择你用的应用,步骤会显示在下方。
可连接 Claude · 约 2 分钟
不需要密钥,不用安装程序 · 在 claude.ai 或桌面应用设置一次,即可在聊天中使用 · 免费账户可添加 1 个 · Team/Enterprise 方案由所有者(Owner)添加
CustomizeConnectors
把这里的内容复制到 Claude 中同名的输入框,然后点击 Continue
Flowlineshttps://api.flowlines.ai/mcp在 Claude 检测到的登录设置页点击 Continue · Authentication 按作者说明选择(Sign in now、Sign in when needed 或 No sign in),然后点击 Add
+ConnectorsFlowlines
用 Flowlines 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude 说正在使用 Flowlines,或回答中出现 Flowlines 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
能否连接 ChatGPT 取决于方案
完整 MCP:网页版 ChatGPT Business、Enterprise 或 Edu · Pro:以读取/获取权限连接,修改数据的工具可能用不了 · Plus:官方文档尚未确认,如果在插件页看到添加自定义 MCP 的选项可以试试 · 仅限网页
Plugins+Add custom MCP server
输入名称,在 Connection 下粘贴地址,勾选风险提示,然后点击 Create as a plugin
Flowlineshttps://api.flowlines.ai/mcp开启新聊天,输入前先从插件列表中选择 Flowlines
用 Flowlines 告诉我你能帮我做什么,然后做一件给我看✓ 如果 ChatGPT 说正在使用 Flowlines,或回答中出现 Flowlines 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 OpenAI 帮助页面核实 · 你的界面可能因版本略有不同
可连接 Claude Code · 约 2 分钟
不需要密钥,不用安装程序
Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell
claude mcp add --transport http --scope user flowlines https://api.flowlines.ai/mcp应能看到 Flowlines 在列表中并显示已连接
用 Flowlines 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude Code 说正在使用 Flowlines,或回答中出现 Flowlines 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
可连接 Cursor · 约 2 分钟
不需要密钥,不用安装程序
用 Flowlines 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Cursor 说正在使用 Flowlines,或回答中出现 Flowlines 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Cursor 帮助页面核实 · 你的界面可能因版本略有不同
可连接 VS Code · 约 2 分钟
不需要密钥,不用安装程序
用 Flowlines 告诉我你能帮我做什么,然后做一件给我看✓ 如果 VS Code 说正在使用 Flowlines,或回答中出现 Flowlines 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 VS Code 帮助页面核实 · 你的界面可能因版本略有不同
其他支持 MCP 的应用(如 Windsurf、Zed、Gemini CLI)添加方式各不相同,请在应用列表中查看
通过网页连接的应用使用的地址
https://api.flowlines.ai/mcpJSON 设置在下方“开发者专用”中
Claude Code
claude mcp add --transport http --scope user flowlines https://api.flowlines.ai/mcpCursor (~/.cursor/mcp.json)
{
"mcpServers": {
"flowlines": {
"url": "https://api.flowlines.ai/mcp"
}
}
}VS Code (mcp.json)
{
"servers": {
"flowlines": {
"type": "http",
"url": "https://api.flowlines.ai/mcp"
}
}
}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。
依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。
通过互联网连接,适用于
ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast
服务器要求先登录,才会列出它的工具。
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15日
注册名称已通过域名 flowlines.ai 验证(注册中心会核实域名所有权)
代码最后更新于5天前
不适用:由 flowlines.ai 的所有者自己运行的云端服务器,你不会运行仓库里的代码
需要你自己复制粘贴密钥或令牌
尚未检查;我们会在检查端点时读取工具列表
提问、分享技巧或报告无法使用。按“有帮助”数量排序。
还没有评价
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。
它在 4 项适用的基础检查中通过了 2 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。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在作者的云端运行。通过互联网连接,无需安装,但你发送的内容会经过作者的服务器。
GitHub 仓库最后更新于5天前,最新版本(2.0.0)于17天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,因此也能用于接受远程 MCP 的应用,例如网页版 ChatGPT(Business 及以上为完整 MCP;Pro 为只读)。
同一分类中有:GitDiagram, Appllama, Cloudflare MCP, Omnara, Repowise
同一分类、其他作者、命令已通过检查。官方的排在前面,再按星标排序。
如何看卡片:↓ 每周下载量i★ 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
Architecture diagrams, explanations and Mermaid source for public GitHub repositories.
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Codebase intelligence for AI coding agents: graph, git history, docs, decisions, code health.
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。
Official Flowlines plugins for MCP observability. This repository ships the portable flowlines plugin, marketplaces for Claude Code and Codex, and a Cursor plugin manifest (plugins/flowlines/.cursor-plugin/plugin.json) for cursor.directory:
| Component | What it does |
|---|---|
flowlines MCP server | Connects your MCP client to your Flowlines workspace at https://api.flowlines.ai/mcp. Inspect how people use your MCP servers, what changed since a release, and where sessions go wrong. Record findings as notes. Its onboarding tool returns an instrumentation plan; check_onboarding checks the telemetry that arrives. |
flowlines-weekly-review skill | A periodic review over the MCP server: what changed since the last review, signals, outcome movements, and what to pin for next time. |
flowlines-release-check skill | Before-and-after comparison of an MCP server release: outcomes, intents, cost, signals, and evidence sessions, with the right denominators. |
flowlines-investigate-session skill | From a signal, a user, or a complaint to the failing turn, with minimal exposure of end-user content. |
flowlines-cohort-builder skill | Sizes a user segment, writes it in the Flowlines cohort rule vocabulary, and compares it against a baseline. |
flowlines-doctor skill | Diagnoses missing or incomplete data across every source: instrumented MCP servers, LangSmith and Langfuse connectors, and OTLP applications. |
save_note and report_outcome tools.onboarding plan exports validated tool arguments, client-visible results, and user identity metadata to Flowlines. That data can contain personal data, customer data, source code, or other sensitive content. You choose what it records on the Flowlines get-started page.reason and user_intent are short sentences that the calling model writes, and they do not need to identify anyone. If your privacy rules do not allow user_intent, keep it and remove the identity instead: send no user.name or user.email, and send an HMAC-SHA-256 of your user ID as user.id. The hash stays the same for one user, so Flowlines still groups the calls into sessions. Without user_intent, only clients that send a session ID get sessions.onboarding and check_onboarding are read-only: they return an instrumentation plan and check the calls that arrive. The namespace API key is created on the Flowlines get-started page and shown once there; never paste it into chat.
The public distribution target is one Flowlines plugin with the hosted MCP server and four shared analysis skills in OpenAI's Plugins Directory. This repository prepares its submission; merging changes does not publish it or install it for users. A public listing and installation reuse across products still require review and verification.
See the shared plugin guide for the build and verification steps, and the submission worksheet for listing assets and review cases. The existing desktop marketplace installs below continue to work. Personal ChatGPT accounts can test the server separately through Developer mode while the public submission is being prepared.
claude plugin marketplace add flowlines-ai/plugins && claude plugin install flowlines@flowlines
Then run /mcp inside Claude Code and sign in to flowlines. Skills are available as /flowlines: , for example /flowlines:flowlines-weekly-review or /flowlines:flowlines-doctor. Add --scope project to the install command to enable the plugin for one repository only.
Open this repository in the ChatGPT desktop app and restart the app after package changes. In the Plugins Directory, select the Flowlines marketplace and install flowlines. The repo catalog at .agents/plugins/marketplace.json points to plugins/flowlines. Complete the MCP sign-in and test in a new chat. Local marketplace support can vary by surface; this does not publish the plugin.
codex plugin marketplace add flowlines-ai/plugins && codex plugin add flowlines@flowlines
Then sign in with codex mcp login flowlines, or open /plugins inside Codex. Skills are available as $ , for example $flowlines-weekly-review or $flowlines-doctor.
The plugin registers an MCP server named flowlines. If you previously added the server by hand under the same name, remove that entry to avoid a duplicate.
Signing in also creates your Flowlines account. When you have no workspace or API key yet, the Flowlines get-started page creates them, then turns green on your server's first tool call. To instrument your MCP server, ask your MCP client to onboard you to Flowlines: it calls onboarding for your namespace, carries out the plan, and verifies the first calls with check_onboarding.
If Flowlines calls repeatedly return Internal error or -32603 without a reconnect prompt, use flowlines-doctor when installed. It checks available client status and relevant local logs before treating the failure as an authentication problem. When OAuth refresh has failed, it uses an available native reconnect action or tells you how to reconnect in your client, then verifies tool access before resuming your request. No access to Flowlines infrastructure or server logs is needed.
Recovery uses the failing client's sign-in flow on Windows, macOS, or Linux. Local CLI sign-in must complete credential storage; hosted connectors use their own connection status. If login succeeds but tool calls still fail, the MCP connection may need to be reloaded. The skill reports the remaining recovery step; it does not restart the app automatically.
The public analysis bundle does not include flowlines-doctor. Its analysis skills use the client's native reconnect action for reported authentication problems and direct unresolved failures to Flowlines support.
Claude Code reads marketplaces and plugins from a repository's .claude/settings.json:
{
"extraKnownMarketplaces": { "flowlines": { "source": { "source": "github", "repo": "flowlines-ai/plugins" } } }, "enabledPlugins": { "flowlines@flowlines": true } }
Codex reads a repository-level marketplace from .agents/plugins/marketplace.json:
{
"name": "my-team", "interface": { "displayName": "My team" }, "plugins": [ { "name": "flowlines", "source": { "source": "git-subdir", "url": "https://github.com/flowlines-ai/plugins.git", "path": "./plugins/flowlines", "ref": "main" }, "policy": { "installation": "INSTALLED_BY_DEFAULT", "authentication": "ON_USE" }, "category": "Developer Tools" } ] }
Version 0.2.0 removes the flowlines-agent-observability skill. If you used it, its settings stay on your machine and continue to send full prompts, assistant messages, and tool content to Flowlines. The plugin no longer ships its uninstall.sh, so remove the settings manually:
~/.claude/settings.json, delete these keys from env: CLAUDE_CODE_ENABLE_TELEMETRY, CLAUDE_CODE_ENHANCED_TELEMETRY_BETA, OTEL_LOGS_EXPORTER, OTEL_TRACES_EXPORTER, OTEL_METRICS_EXPORTER, OTEL_EXPORTER_OTLP_PROTOCOL, OTEL_EXPORTER_OTLP_ENDPOINT, OTEL_EXPORTER_OTLP_HEADERS, OTEL_LOG_USER_PROMPTS, OTEL_LOG_ASSISTANT_RESPONSES, OTEL_LOG_TOOL_DETAILS, and OTEL_LOG_TOOL_CONTENT.~/.codex/config.toml, delete environment, log_user_prompt, and the exporter that contains x-flowlines-api-key from [otel]. Delete hooks = true from [features] only if you use no other Codex hooks. In ~/.codex/hooks.json, delete the UserPromptSubmit, PostToolUse, and Stop entries whose command is "$HOME/.local/lib/flowlines-agent-observability/codex-hook-relay.sh".~/.local/lib/flowlines-agent-observability/.${XDG_CONFIG_HOME:-~/.config}/flowlines-agent-observability/ holds copies of your original configuration files in originals/, a curl.conf that contains your API key, and a spool/ of unsent Codex events that can contain prompt content. If you had your own OpenTelemetry settings before the install, restore them from originals/. Then delete the folder.Start new Claude Code and Codex sessions to apply the changes.
.claude-plugin/marketplace.json Claude Code marketplace
.agents/plugins/marketplace.json Codex marketplace server.json Official MCP Registry entry plugins/flowlines/ plugin.json Portable identity and OpenAI listing metadata mcp.json Portable MCP server (streamable-http) .claude-plugin/plugin.json Claude Code manifest .codex-plugin/plugin.json Codex compatibility manifest .mcp.json Compatibility MCP server (http) assets/ Shared icons skills/ Shared skills
The portable package follows OpenAI's packaging guide. Hosts discover skills/ and mcp.json at the plugin root. OpenAI listing fields live under extensions.com.openai.interface in plugin.json. That extension replaces the compatibility overlay; the two are not merged. Keep identity, listing fields, versions, and MCP endpoints in sync with the compatibility files. The tests check these values. Existing clients can still use their original manifests and .mcp.json.
Validate the manifests with the real CLIs, then the skill packages:
scripts/validate_plugins.sh
python3 scripts/validate_skills.py python3 -m unittest discover -s scripts -p 'test_public_submission.py'
validate_plugins.sh needs claude and codex on your PATH. It runs offline: Claude validates the manifests, and Codex installs the public bundle and repo plugin separately in a temporary home. Each install must register exactly one MCP server at https://api.flowlines.ai/mcp. These checks do not complete OAuth or call the server.
To try the plugin from a checkout without installing it, run claude --plugin-dir plugins/flowlines, or add this directory as a local marketplace with codex plugin marketplace add ..
Plugin 0.5.0 uses the onboarding and check_onboarding tools from MCP server 2.0.0. Deploy that server version before releasing this plugin. The old onboard tool is retired; the user-invoked MCP prompt named onboard remains available.
version in plugins/flowlines/plugin.json, plugins/flowlines/.claude-plugin/plugin.json, plugins/flowlines/.codex-plugin/plugin.json, and the plugin entry in .claude-plugin/marketplace.json.main. Marketplace installs track main; users pick up the new version with claude plugin update flowlines@flowlines or codex plugin marketplace upgrade.claude plugin tag plugins/flowlines.Public Plugins Directory releases use the separate submission and publication process. A repository merge or tag does not publish that listing.
server.json lists the remote MCP server, not the plugin, on the [official MCP Registry](https://registry.modelcont