rpg-encoder MCP server

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.

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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 The CLI lifts entities with no features; re-lifting stale entities (features present but outdated after code changes) is handled by the in-session MCP flow, not the CLI.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"

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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.

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How It Works

1. Parse — Tree-sitter extracts entities (functions, classes, methods) and dependency edges (imports, calls, inheritance) from 15 languages.
2. 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".
3. Organize — Features cluster into a 3-level semantic hierarchy (Area → Category → Subcategory) that emerges from what the code does, not the file tree.
4. Understand — semantic_snapshot compresses 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.

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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

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MCP Tools (27)

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

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

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

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")

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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

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Install

MCP server (recommended)

# Claude Code
claude mcp add rpg -- npx -y -p rpg-encoder rpg-mcp-server

# Cursor — add to ~/.cursor/mcp.json
{
  "mcpServers": {
    "rpg": {
      "command": "npx",
      "args": ["-y", "-p", "rpg-encoder", "rpg-mcp-server"]
    }
  }
}

The server auto-detects the project root from the current working directory — no path argument needed.

npm install -g rpg-encoder

# Build a graph
rpg-encoder build

# Query
rpg-encoder search "parse entities from source code"
rpg-encoder fetch "src/parser.rs:extract_entities"
rpg-encoder explore "src/parser.rs:extract_entities" --direction both --depth 2
rpg-encoder info

# Autonomous lifting via API
rpg-encoder lift --provider anthropic --dry-run  # estimate cost
rpg-encoder lift --provider anthropic           # lift with Haiku (~$0.02/100 entities)

# Incremental update
rpg-encoder update

# Pre-commit hook (auto-updates graph on commit)
rpg-encoder hook install

git clone https://github.com/userFRM/rpg-encoder.git
cd rpg-encoder && cargo build --release

Then point your MCP config at target/release/rpg-mcp-server.

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Documentation

• How RPG Compares — honest comparison with GitNexus, Serena, Repomix, and others
• Paper Fidelity — algorithm-by-algorithm comparison with the research paper
• Use Cases — practical examples of what RPG enables
• CHANGELOG — release history

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Inspirations & References

rpg-encoder is built on the theoretical framework from the RPG-Encoder research paper, with original extensions inspired by tools across the code intelligence landscape:

• RPG-Encoder paper (Luo et al., 2026, Microsoft Research) — semantic lifting model, 3-level hierarchy construction, incremental evolution algorithms, formal graph model G = (V_H ∪ V_L, E_dep ∪ E_feature).
• GitNexus — precomputed relational intelligence, blast radius analysis, Claude Code hooks. Showed that a code graph tool must be invisible to be essential.
• Serena — symbol-level precision via LSP. Demonstrated that real-time code awareness matters more than batch analysis.
• TOON — Token-Oriented Object Notation for LLM-optimized output.

This is an independent implementation. All code is original work under the MIT license. Not affiliated with or endorsed by Microsoft.

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License

MIT