Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Who calls this, what breaks, which callers an edit missed: labelled answers. Windows, Python, TS/JS.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"llmgraph": {
"command": "npx",
"args": [
"-y",
"llmgraph-mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add llmgraph -- npx -y llmgraph-mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"llmgraph": {
"command": "npx",
"args": [
"-y",
"llmgraph-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"llmgraph": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"llmgraph-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub brainfoods-studio, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.4.7) llegó al registro ayer.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Claude Checkpoint MCP
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
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MCP server that checkpoints Claude Code session context to survive crashes and token limits
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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