Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Who calls this, what breaks, which callers an edit missed: labelled answers. Windows, Python, TS/JS.
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。
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。
选择你使用的 AI 应用,然后复制设置。
你需要
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": {
"llmgraph": {
"command": "npx",
"args": [
"-y",
"llmgraph-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add llmgraph -- npx -y llmgraph-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"llmgraph": {
"command": "npx",
"args": [
"-y",
"llmgraph-mcp"
]
}
}
}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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"llmgraph": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"llmgraph-mcp"
]
}
}
}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。
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。
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7日
注册名称已通过 GitHub 账户 brainfoods-studio 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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 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。
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)。
GitHub 仓库最后更新于–,最新版本(0.4.7)于昨天进入注册中心。
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。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Agent Data Tools: Utilities
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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
103 tools: JSON repair, CSV, hashes, cron, dates, units, validators. 25 free calls.
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。
Every call graph has blind spots. Ours tells you where they are.
A local MCP server and Claude Code plugin that tells your coding agent who calls a function, what breaks if it changes, which callers an edit missed, and what it can't see. Windows x64; Python and TypeScript/JavaScript. Real answers, no sign-up: beta.llmgraph.in/docs/real-answers.
LLMGRAPH inks only the calls it can prove. The ones it can't see, it counts and names in plain words, so your agent sees the blind spots before it edits.
I built LLMGRAPH as the truth layer for coding agents, and the truth includes the blind spots: agents guess; LLMGRAPH proves what it can and names what it can't. In a blind test of 50 questions on three open-source repos, no confident answer was wrong.
grep reads names. LLMGRAPH follows calls. What it can prove, it proves; what it can't, it tells you, down to the line where it stopped.
zod ships two copies of addIssueToContext. A text search mixes them together:
$ rg -n addIssueToContext # zod v3.23.8
146 lines, from both copies, mixed together
LLMGRAPH separates them, and shows its work:
llmgraph · who_calls addIssueToContext (src/helpers/parseUtil.ts:72): 71 in a set
⚠︎ type checker starting: answers use our own reading for now; 71 calls unchecked ◇ within_these 71 calls -> one of 1 (closed: repo only) [c] w1 ⚠︎ plus 7 dynamic calls nearby that could reach anything w2 – ruled out: 71 same-name calls (proven: other target) [c] w3 assumes [c]: our own reading, not confirmed by the checker (lines trimmed)
71 look-alikes ruled out, and it shows why. And where the type checker hasn't confirmed yet, it says so.
Before your agent pulls a thread, it should know. In requests, change super_len(o) to super_len(o, strict), and ask check_my_changes:
llmgraph · check_my_changes: 1 file changed, 16 calls break
16 breaks: misses "strict" w1 ⚠︎ breaks if it runs super_len src/requests/models.py:526 PreparedRequest.prepare_body (within_these, one of 1, closed: repo only) [c] w2 ⚠︎ breaks if it runs super_len src/requests/models.py:575 PreparedRequest.prepare_content_length (within_these, one of 1, closed: repo only) [c] w3 ⚠︎ breaks if it runs super_len tests/test_utils.py:62 TestSuperLen.test_io_streams (within_these, one of 1, closed: repo only) [c] w4 … 13 more test call sites ⚠︎ plus 9 dynamic calls nearby that could reach super_len w18 (lines trimmed)
16 broken calls named, exactly the real ones. A blind tester's text search found the same 16, no more and no fewer. And the 9 dynamic calls it can't follow are marked, not ignored.
Then the type checker catches up. Ask again a few seconds later:
llmgraph · who_calls super_len (src/requests/utils.py:135): 16 certain
✓ certain src/requests/models.py:526 PreparedRequest.prepare_body w33 +1 more (all certain, same file) w34 ✓ certain tests/test_utils.py:62 TestSuperLen.test_io_streams w35 +13 more (all certain, same file) w36 ⚠ plus 9 dynamic calls nearby that could reach anything w37 (lines trimmed)
From "our own reading" to 16 `certain`, on the released program. Its labels never say more than it has proven, and they upgrade the moment it can.
Other tools sketch your codebase from rumour and fill the blind spots with confidence. LLMGRAPH works in two colours: ink for what it has proven, a plain warning for what it hasn't, and never one dressed up as the other.
| Label | What it means |
|---|---|
certain | We can prove it is exactly one function. |
exactly_these | A known set, and every member really can run here. |
within_these | One of a known list; some listed may never happen here. |
maybe_more | There are candidates, but the door is open. |
cannot_see | Nothing to go on. |
Answers carry no numbers about chance; they say what is ruled out.
Callers are confirmed by the type checker in under a second after your agent asks (Python), and check_my_changes answers in under a second once LLMGRAPH has caught up with a save. On names that also appear in unrelated code, it named no wrong callers where a plain text search returned hundreds of wrong lines, and the agent read less than half as many tokens; on clean, unique names there is no saving.
How we measured, the raw data, and where LLMGRAPH loses: BENCHMARKS.md.
See real answers, no sign-up needed: beta.llmgraph.in/docs/real-answers.
| Tool | What it answers |
|---|---|
who_calls | Every place that calls this function, plus calls we can't see. |
what_breaks_if_changed | Everything that depends on this, directly or indirectly, and what we can't see. |
check_my_changes | Manual check of edits: callers you didn't update, and anything we can't see. |
what_is_unknown | Where our view is blind (dynamic calls, unknown types, unreadable files) and why. |
explain | Why we said this: the evidence behind one answer line. |
Your code is read and analysed on your machine, and we never receive your code, paths, symbol or repo names, queries or answers. Checked on the released program: it contacts only beta.llmgraph.in. Usage counts and crash reports are on by default and can be switched off (npx -y llmgraph-mcp telemetry off); the licence check is always on. Privacy page.
Claude Code (plugin):
claude plugin marketplace add brainfoods-studio/llmgraph-agent-plugin
claude plugin install llmgraph@llmgraph
The plugin offers LLMGRAPH's answer before your agent searches for a function or class name, and never blocks the search. When your agent is about to finish, it stops it once if the saved edits left calls that are certain to break, and lists them; it never blocks on anything less, and npx -y llmgraph-mcp hook off turns both off.
Any MCP agent: add the server npx -y llmgraph-mcp (on Windows some agents need cmd /c npx -y llmgraph-mcp), sign in once with Google, and your agent asks. Setup guides for Claude Code, Antigravity, Cursor, Codex, Gemini CLI, Windsurf and OpenCode; tested by us on Claude Code and Antigravity.
Which project it reads: the first answer of each session names it (project: (from )). Without --repo it is the folder your agent names, else the folder the server started in. If that is not your project, reconnect llmgraph (in Claude Code, /mcp), or name the folder with --repo or LLMGRAPH_REPO in a project .mcp.json (the plugin's own entry names none, as it serves every project):
{
"mcpServers": { "llmgraph": { "command": "npx", "args": ["-y", "llmgraph-mcp", "--repo", "C:\\work\\my-project"] } } }
Today's limits: Windows x64 · Python and TypeScript/JavaScript (other languages are reported as cannot_see, not counted as nothing) · repos up to the plan's line limit · invite-only beta, request an invite · one plan for individuals with a trial, prices.
This repo holds the open setup files under MIT; the LLMGRAPH engine is closed source and runs on your machine under the EULA. Brainfoods Studio, India · founder@llmgraph.in · security@llmgraph.in