Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Semantic code graph for AI-assisted code understanding via tree-sitter and MCP.
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": {
"rpg-encoder": {
"command": "npx",
"args": [
"-y",
"rpg-encoder"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add rpg-encoder -- npx -y rpg-encoderPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"rpg-encoder": {
"command": "npx",
"args": [
"-y",
"rpg-encoder"
]
}
}
}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": {
"rpg-encoder": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"rpg-encoder"
]
}
}
}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 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub userFRM, 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 (0.6.7) llegó al registro hace 8 meses.
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, 오늘냥이 (Oneul Nyangi)
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.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Real cat photos, diaries, profiles, age and food reference calculators, and breeds.
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.
rpg-encoder
Give your AI agent a brain for your codebase.
AI coding agents waste most of their tool calls fumbling through your codebase with grep, cat, find, and file reads. rpg-encoder fixes that. It builds a semantic graph of your code with Tree-sitter — not just *what calls what*, but *what every function does* — and gives your AI assistant whole-repo understanding via MCP in a single tool call.
claude mcp add rpg -- npx -y -p rpg-encoder rpg-mcp-server
One command. Works with Claude Code, Cursor, opencode, Windsurf, or any MCP-compatible agent. No Rust toolchain, no cloning, no building — npx downloads a pre-built binary for your platform.
Then open any repo and tell your agent:
*"Build and lift the RPG for this repo"*
Your agent handles everything: indexes entities (seconds), reads each function and adds intent-level features (a few minutes), organizes them into a semantic hierarchy, and commits .rpg/graph.json for your team.
For repos with ~100+ entities, lifting_status will tell your agent to delegate the lifting loop to a sub-agent or a cheaper model — feature extraction is pattern-matching, not novel reasoning. If your runtime has no sub-agent mechanism, run rpg-encoder lift --provider anthropic|openai from the terminal with an API key — the CLI drives an external LLM directly with no agent involvement. After the CLI finishes, call reload_rpg in your session to load the updated graph. The CLI lifts entities with no features; re-lifting stale entities (features present but outdated after code changes) is handled by the in-session MCP flow, not the CLI.
Once lifted, try:
grep, cat, findThe server instructions tell your agent to reach for RPG tools FIRST for any question about code structure or behavior. That reflex matters — grep, cat, and ad-hoc file reads burn tokens and miss semantic relationships RPG already knows.
| If you'd otherwise reach for... | Use this instead |
|---|---|
grep -r / rg (by intent) | search_node(query="...") |
grep -r / rg (by name) | search_node(query="...", mode="snippets") |
cat / reading a function | fetch_node(entity_id="file:name") |
| chained greps for callers/callees | explore_rpg(entity_id="...", direction="...") |
| recursive grep for "what depends on X" | impact_radius(entity_id="...") |
wc -l / find / tree | rpg_info |
| reading many files for context | semantic_snapshot |
| manual search → fetch → explore chains | context_pack(query="...") |
| "how do I refactor X safely" | plan_change(goal="...") |
Fall back to grep, cat, or file reads only when the query is about literal text (string search, comments, TODOs, log messages) — not about structure.
semantic_snapshot compresses the whole graph into ~25K tokens. Your LLM reads it once and *knows the repo*.Instead of grepping through files, the LLM calls semantic_snapshot once and receives:
~25K tokens covers ~1000 entities. That's 2-3% of a 1M context window — the LLM starts every session already knowing your repo.
Whenever your working tree changes — committed, staged, or unstaged — the MCP server automatically re-syncs before responding to the next query. A changeset hash over (path, size, mtime) means repeated saves of the same file trigger one sync, and idle queries trigger none. Reverts are detected too: if a previously-dirty file returns to its HEAD state, the graph is restored.
| Mode | Command | Cost | Who pays |
|---|---|---|---|
| Agent lifting | *"Build and lift the RPG"* | Subscription tokens | Your Claude Code / Cursor subscription |
| Autonomous lifting | auto_lift(provider="anthropic", api_key_env="ANTHROPIC_API_KEY") | ~$0.02 per 100 entities | External API key (Haiku, GPT-4o-mini, OpenRouter, Gemini) |
auto_lift calls a cheap external LLM directly — your coding subscription never touches the lifting work. Use api_key_env to resolve keys from environment variables so they never appear in tool call transcripts.
Seven Rust crates, one MCP server binary, one CLI binary:
| Crate | Role |
|---|---|
rpg-core | Graph types (RPGraph, Entity, HierarchyNode), storage, LCA algorithm |
rpg-parser | Tree-sitter entity + dependency extraction (15 languages) |
rpg-encoder | Encoding pipeline, lifting utilities, incremental evolution |
rpg-nav | Search, fetch, explore, snapshot, TOON serialization |
rpg-lift | Autonomous LLM lifting (Anthropic, OpenAI, OpenRouter, Gemini) |
rpg-cli | CLI binary (rpg-encoder) |
rpg-mcp | MCP server binary (rpg-mcp-server) with 27 tools |
Build & Maintain (4 tools)
| Tool | Description |
|---|---|
build_rpg | Index the codebase (run once, instant) |
update_rpg | Incremental update from git changes |
reload_rpg | Reload graph from disk after external changes |
rpg_info | Graph statistics, hierarchy overview, per-area lifting coverage |
Navigate & Search (5 tools)
| Tool | Description |
|---|---|
semantic_snapshot | Whole-repo semantic understanding in one call (~25K tokens for 1000 entities) |
search_node | Search entities by intent or keywords (hybrid embedding + lexical scoring) |
fetch_node | Get entity metadata, source code, dependencies, and hierarchy context |
explore_rpg | Traverse dependency graph (upstream, downstream, or both) |
context_pack | Single-call search + fetch + explore with token budget |
Plan & Analyze (7 tools)
| Tool | Description |
|---|---|
impact_radius | BFS reachability analysis — "what depends on X?" |
plan_change | Change planning — find relevant entities, modification order, blast radius |
find_paths | K-shortest dependency paths between two entities |
slice_between | Extract minimal connecting subgraph between entities |
analyze_health | Code health: coupling, instability, god objects, clone detection |
detect_cycles | Find circular dependencies and architectural cycles |
reconstruct_plan | Dependency-safe reconstruction execution plan |
Semantic Lifting (11 tools)
| Tool | Description |
|---|---|
auto_lift | One-call autonomous lifting via cheap LLM API (Haiku, GPT-4o-mini, OpenRouter, Gemini) |
lifting_status | Dashboard — coverage, per-area progress, NEXT STEP |
get_entities_for_lifting | Get entity source code for your agent to analyze |
submit_lift_results | Submit the agent's semantic features back to the graph |
finalize_lifting | Aggregate file-level features, rebuild hierarchy metadata |
get_files_for_synthesis | Get file-level entity features for holistic synthesis |
submit_file_syntheses | Submit holistic file-level summaries |
build_semantic_hierarchy | Get domain discovery + hierarchy assignment prompts |
submit_hierarchy | Apply hierarchy assignments to the graph |
get_routing_candidates | Get entities needing semantic routing (drifted or newly lifted) |
submit_routing_decisions | Submit routing decisions (hierarchy path or "keep") |
15 languages via Tree-sitter:
| Language | Entity Extraction | Dependency Resolution |
|---|---|---|
| Python | Functions, classes, methods | imports, calls, inheritance |
| Rust | Functions, structs, traits, impl methods | use, calls, trait impls |
| TypeScript | Functions, classes, methods, interfaces | imports, calls, inheritance |
| JavaScript | Functions, classes, methods | imports, calls, inheritance |
| Go | Functions, structs, methods, interfaces | imports, calls |
| Java | Classes, methods, interfaces | imports, calls, inheritance |
| C / C++ | Functions, classes, methods, structs | includes, calls, inheritance |
| C# | Classes, methods, interfaces | using, calls, inheritance |
| PHP | Functions, classes, methods | use, calls, inheritance |
| Ruby | Classes, methods, modules | require, calls, inheritance |
| Kotlin | Functions, classes, methods | imports, calls, inheritance |
| Swift | Functions, classes, structs, protocols | imports, calls, inheritance |
| Scala | Functions, classes, objects, traits | imports, calls, inheritance |
| Bash | Functions | source, calls |
# Claude Code
claude mcp add rpg -- npx -y -p rpg-encoder rp
Solo se muestra el principio; lee el resto en el repo