chuk-mcp-code-raptor
Deep code intelligence for MCP — an MCP server that gives AI agents semantic understanding of codebases via RAPTOR hierarchical indexing and code property graphs.
README
chuk-mcp-code-raptor
Deep code intelligence for MCP — an MCP server that gives AI agents semantic understanding of codebases via RAPTOR hierarchical indexing and code property graphs.
Pure intelligence, no filesystem. This server only provides capabilities the client doesn't already have — semantic search, dependency graphs, hierarchical context, AST-aware symbol lookup.
What It Does
Every MCP client (Claude Code, Cursor, etc.) already has file reading, text search, and shell access. This server adds the intelligence layer on top:
| What clients have | What this server adds |
|---|---|
| Keyword search (grep) | Semantic search — "how does auth work" finds the right code across abstraction levels |
| File reading | Hierarchical context — where a symbol sits in the architecture, what it affects |
| Symbol grep | AST-aware symbol lookup — knows the difference between a class and a function with the same name |
| Manual exploration | Dependency graphs — what imports what, data flow, blast radius analysis |
| Nothing | Project detection — auto-detect language, framework, test runner, package manager |
| Nothing | Code outline — symbols with signatures, line numbers, docstrings |
Tools
10 tools across 5 groups. All return structured Pydantic JSON, not raw file contents.
Session — Project Selection (1 tool)
| Tool | Description |
|---|---|
set_project |
Set the active project directory and build the index. Falls back to CODE_RAPTOR_PROJECT env var if no path given. Must be called before other tools. |
Orient — Project Awareness (2 tools)
| Tool | Description |
|---|---|
get_project_info |
Detect language, framework, package manager, test framework, entry points |
get_outline |
Show symbols in a file or directory with line numbers, signatures, docstrings |
Find — Search & Discovery (3 tools)
| Tool | Description |
|---|---|
search_semantic |
Semantic code search across RAPTOR hierarchy levels. Supports max_results, token_budget |
find_symbol |
Find a class, function, or method by name. Optional kind filter |
find_references |
Find everywhere a symbol is used — imports, calls, data flow |
Understand — Context & Relationships (2 tools)
| Tool | Description |
|---|---|
get_context |
Hierarchical context — where a symbol sits in the architecture, related components, impact scope |
get_dependencies |
Import and data-flow graph — what a symbol depends on and what depends on it |
Maintenance — Index Management (2 tools)
| Tool | Description |
|---|---|
reindex |
Full index rebuild after major changes |
reindex_file |
Incremental update after editing a single file |
All intelligence tools are read-only (readOnlyHint=True). Session and maintenance tools are idempotent (idempotentHint=True).
Installation
Using uv (Recommended)
# Install from PyPI
uv pip install chuk-mcp-code-raptor
# Or clone and install from source
git clone https://github.com/chrishayuk/chuk-mcp-code-raptor.git
cd chuk-mcp-code-raptor
uv sync --dev
Using pip
pip install chuk-mcp-code-raptor
Optional dependencies
# Local embeddings (sentence-transformers, recommended)
pip install "chuk-mcp-code-raptor[embeddings-local]"
# OpenAI embeddings
pip install "chuk-mcp-code-raptor[embeddings-openai]"
# Anthropic summarization (Phase 2)
pip install "chuk-mcp-code-raptor[summarization-anthropic]"
Usage
With mcp-cli (uv)
Add to your server_config.json or ~/.mcp.json:
{
"servers": {
"code-raptor": {
"command": "uv",
"args": ["run", "--directory", "/path/to/chuk-mcp-code-raptor", "chuk-mcp-code-raptor"],
"type": "stdio"
}
}
}
Then in the chat, call set_project to choose a codebase:
💬 You: set_project to /path/to/my/repo then tell me the architecture
With Claude Desktop
Add to your Claude Desktop configuration:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"code-raptor": {
"command": "chuk-mcp-code-raptor",
"env": {
"CODE_RAPTOR_PROJECT": "/path/to/your/project"
}
}
}
}
With CODE_RAPTOR_PROJECT set, call set_project() (no argument) to auto-initialize from the env var.
Standalone
# STDIO mode (default, for MCP clients)
python -m chuk_mcp_code_raptor
# HTTP mode (for web access)
python -m chuk_mcp_code_raptor http
From Python
from chuk_mcp_code_raptor.config import ServerConfig
from chuk_mcp_code_raptor.state import ServerState, set_state
from chuk_mcp_code_raptor.tools.find import search_semantic
# Initialize the index
config = ServerConfig(target_repo="/path/to/project")
state = ServerState(config=config)
await state.initialize()
set_state(state)
# Semantic search
result = await search_semantic("how does authentication work")
Examples
Four runnable demos in the examples/ directory:
# Tool registration and schema inspection
uv run examples/server_demo.py
# Agent workflow with hardcoded data (no indexing required)
uv run examples/agent_workflow_demo.py
# Full indexing pipeline — creates a project, indexes it, calls all 9 tools
uv run examples/live_indexing_demo.py
# MCP protocol interaction via ToolRunner
uv run examples/mcp_client_demo.py
| Demo | What it shows |
|---|---|
server_demo.py |
Tool registration, schemas, MCP hints |
agent_workflow_demo.py |
How an agent would use the tools in sequence |
live_indexing_demo.py |
Full RAPTOR + CPG pipeline on a realistic project |
mcp_client_demo.py |
MCP protocol calls through ToolRunner |
Development
Setup
git clone https://github.com/chrishayuk/chuk-mcp-code-raptor.git
cd chuk-mcp-code-raptor
uv sync --dev
Running Tests
make test # Run tests
make test-cov # Run tests with coverage
make coverage-report # Show coverage report
Code Quality
make lint # Run linters (ruff)
make format # Auto-format code
make typecheck # Run type checking (mypy)
make security # Run security checks (bandit)
make check # Run all checks (lint + typecheck + security + test)
Building
make build # Build package
make version # Show current version
make bump-patch # Bump patch version
make publish # Create tag and trigger automated release
Architecture
src/chuk_mcp_code_raptor/
├── __init__.py
├── __main__.py # python -m chuk_mcp_code_raptor
├── server.py # MCP server instance, tool registration
├── config.py # ServerConfig (Pydantic)
├── state.py # ServerState — holds index, CPG, RAPTOR builder
├── constants.py # All enums and constants (no magic strings)
├── protocols.py # Structural typing protocols
├── models/ # Pydantic models for tool I/O
│ ├── orient.py # ProjectInfo, SymbolInfo, OutlineResult
│ ├── find.py # SemanticMatch, SymbolMatch, ReferenceLocation
│ ├── understand.py # HierarchyContext, DependencyGraph
│ └── maintenance.py # ReindexResult, FileReindexResult
├── tools/ # Tool handlers (pure async functions)
│ ├── session.py # set_project
│ ├── orient.py # get_project_info, get_outline
│ ├── find.py # search_semantic, find_symbol, find_references
│ ├── understand.py # get_context, get_dependencies
│ └── maintenance.py # reindex, reindex_file
├── indexing/ # Index pipeline
│ ├── pipeline.py # Orchestration: scan → chunk → embed → RAPTOR → CPG
│ ├── scanner.py # Project detection (language, framework, tests)
│ ├── converters.py # chuk-code-raptor ↔ Pydantic adapters
│ └── providers/
│ ├── embeddings.py # EmbeddingProvider protocol + implementations
│ └── summarization.py # SummarizationProvider protocol (Phase 2)
└── utils/
├── async_bridge.py # run_sync() — wraps sync calls in executor
├── paths.py # Path resolution and validation
├── subprocess.py # Async subprocess runner
└── diff.py # Unified diff generation
Design Principles
- Async native — every I/O-touching function is
async def - Pydantic native — all data boundaries use typed models, not raw dicts
- No magic strings — every repeated string is an enum or constant
- Composable — tools don't know about transport, indexing doesn't know about MCP
- Pure intelligence — no file reading, no shell, no git — only what clients can't do themselves
Dependencies
| Package | Role |
|---|---|
| chuk-mcp-server | MCP framework (@tool decorator, transports, ToolRunner) |
| chuk-code-raptor | RAPTOR hierarchy, CPG, chunking engine, intelligent search |
| pydantic | Data validation and serialization |
| tree-sitter-python | Python AST parsing |
Roadmap
See ROADMAP.md for the full phased delivery plan.
- Phase 0 — Scaffold (complete)
- Phase 1 — Working Intelligence (complete)
- Phase 1.5 — MCP Client Integration (complete)
- Phase 2 — LLM Summarization
- Phase 3 — File Watching & Persistence
- Phase 4 — Production Hardening
License
Apache License 2.0 — see LICENSE for details.
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