Rpg Encoder
What it does
Semantic code graph for AI-assisted code understanding via tree-sitter and MCP.
- Connects 在你的电脑上运行
- Account or key No key needed
- Category 开发者工具
安装
选择你使用的 AI 应用,然后复制设置。
通过 2/2 项检查 · 在你的电脑上运行
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,但需要先准备
需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
打开电脑版 Claude 应用的设置文件
SettingsDeveloperEdit Config
粘贴这些设置并保存文件
{ "mcpServers": { "rpg-encoder": { "command": "npx", "args": [ "-y", "rpg-encoder" ] } } }退出 Claude 再重新打开
试一试
用 Rpg Encoder 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 Rpg Encoder,或回答中出现 Rpg Encoder 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
ChatGPT 暂时无法连接 Rpg Encoder
Rpg Encoder 需要安装在你的电脑上,而 ChatGPT 只能连接在网上运行的服务器
你可以改用这些方式
A · 使用能连接 Rpg Encoder 的应用
- 需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
- 需要先在电脑上安装 Node.js(只需一次)
- 需要先在电脑上安装 Node.js(只需一次)
- 需要先在电脑上安装 Node.js(只需一次)
B · 开发者工具 类中能连接 ChatGPT 的其他服务器
从同类中使用较多的里挑选,仅限用地址即可连接、无需密钥的
- Sentry✓ ChatGPT
- Compiler Explorer✓ ChatGPT
- GitDiagram✓ ChatGPT
可连接 Claude Code,但需要先准备
需要先在电脑上安装 Node.js(只需一次)
打开终端
Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell
粘贴这条命令并按 Enter
claude mcp add --scope user rpg-encoder -- npx -y rpg-encoder在 Claude Code 中输入 /mcp
应能看到 Rpg Encoder 在列表中并显示已连接
试一试
用 Rpg Encoder 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 Rpg Encoder,或回答中出现 Rpg Encoder 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,但需要先准备
需要先在电脑上安装 Node.js(只需一次)
在 Cursor 中以 Agent 模式打开 AI 聊天
试一试
用 Rpg Encoder 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 Rpg Encoder,或回答中出现 Rpg Encoder 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,但需要先准备
需要先在电脑上安装 Node.js(只需一次)
在 VS Code 中以 Agent 模式打开 AI 聊天
试一试
用 Rpg Encoder 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 Rpg Encoder,或回答中出现 Rpg Encoder 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
开发者专用:终端命令和 JSON 设置
安装在电脑上
Claude Code
claude mcp add --scope user rpg-encoder -- npx -y rpg-encoderClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"rpg-encoder": {
"command": "npx",
"args": [
"-y",
"rpg-encoder"
]
}
}
}Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"rpg-encoder": {
"command": "npx",
"args": [
"-y",
"rpg-encoder"
]
}
}
}VS Code (mcp.json)
{
"servers": {
"rpg-encoder": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"rpg-encoder"
]
}
}
}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。
一览
哪些应用可以使用它
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。
在你的电脑上运行,适用于
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
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12日
- ✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 userFRM 验证,该账户拥有此仓库
- ✓ 通过
仍在维护
代码最后更新于6个月前
- … 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
讨论
提问、分享技巧或报告无法使用。按“有帮助”数量排序。
用户评价
还没有评价
与“开发者工具”中的其他服务器比较i
- 星标数多于此分类中其他 5810 个服务器的 94%
- 发布到注册中心的时间比此分类中其他 5810 个服务器的 4% 更新
- 基础检查结果等于或优于此分类中其他 5810 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
Rpg Encoder 安全吗?
它在 2 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。
它需要哪些密钥或代码?
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6个月前,最新版本(0.6.7)于9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。
同类别的其他选择
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 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
GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Serena MCP: the IDE for your agent
A powerful toolkit for coding, providing semantic retrieval and editing capabilities.
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。
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.
Quick Start
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:
- *"What handles authentication?"* — finds code even when nothing is named "auth"
- *"Show everything that depends on the database connection"*
- *"Plan a change to add rate limiting to API endpoints"*
Use RPG before grep, cat, find
The 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.
How It Works
- Parse — Tree-sitter extracts entities (functions, classes, methods) and dependency edges (imports, calls, inheritance) from 15 languages.
- Lift — An LLM (your agent, or a cheap API like Haiku) reads each entity and writes verb-object features: *"validate JWT tokens"*, *"serialize config to disk"*.
- Organize — Features cluster into a 3-level semantic hierarchy (Area → Category → Subcategory) that emerges from *what the code does*, not the file tree.
- Understand —
semantic_snapshotcompresses the whole graph into ~25K tokens. Your LLM reads it once and *knows the repo*.
The semantic snapshot
Instead of grepping through files, the LLM calls semantic_snapshot once and receives:
- Hierarchy — every functional area with aggregate features
- Entities — every function, class, method grouped by area, with its semantic features
- Dependency skeleton — condensed call graph with qualified names
- Hot spots — top 10 most-connected entities (the architectural backbone)
~25K tokens covers ~1000 entities. That's 2-3% of a 1M context window — the LLM starts every session already knowing your repo.
Self-maintaining graph
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.
Two ways to lift
| 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.
Architecture
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 |
MCP Tools (27)
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") |
Supported Languages
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 |
Install
MCP server (recommended)
# Claude Code
claude mcp add rpg -- npx -y -p rpg-encoder rp