Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Semantic code graph for AI-assisted code understanding via tree-sitter and MCP.
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"rpg-encoder": {
"command": "npx",
"args": [
"-y",
"rpg-encoder"
]
}
}
}Run this in your terminal
claude mcp add rpg-encoder -- npx -y rpg-encoderPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"rpg-encoder": {
"command": "npx",
"args": [
"-y",
"rpg-encoder"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"rpg-encoder": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"rpg-encoder"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
The registry name was verified with the GitHub account userFRM, which owns the repo
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This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.6.7) reached the registry 8 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, dotnet-mcp
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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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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
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