codebase-indexer
A semantic codebase indexer MCP server that chunks source code, generates embeddings via Ollama, and stores them in Qdrant for natural-language code search.
README
codebase-indexer
/\_/\ codebase-indexer
( •.•) ─────────────────
⊂/ 🌰 \⊃ Semantic code search
/| |\ powered by AI
A semantic codebase indexer that chunks your source code, generates embeddings via Ollama, and stores them in Qdrant for natural-language code search. Ships as an MCP server for Claude Code and Codex integration, and also works as a standalone CLI.
Features
- Semantic search — find code by meaning, not just keywords (
"retry logic with exponential backoff") - 18 languages with symbol-aware chunking (functions, classes, interfaces, etc.)
- 70+ file extensions recognized
- Incremental indexing — only re-indexes changed files (MD5 content hashing)
- MCP server — plug directly into Claude Code or Codex as a tool provider
- File watcher — auto-reindex on save (500ms debounce)
- Interactive CLI — squirrel mascot, spinners, colored output
- Zero config — sensible defaults, works out of the box with
npx
Quick Start
One-Line Install
The fastest way to get started — checks prerequisites, sets up infrastructure, indexes your project, and configures integrations interactively:
bash <(curl -fsSL https://raw.githubusercontent.com/ygtdgn/codebase-indexer/main/install.sh)
Or equivalently:
curl -fsSL https://raw.githubusercontent.com/ygtdgn/codebase-indexer/main/install.sh | bash
Manual Setup
Prerequisites
1. Set up infrastructure
npx codebase-indexer init
This starts a Qdrant container via Docker Compose, verifies your Ollama connection, and pulls the embedding model if needed.
2. Index your project
npx codebase-indexer index ./your-project
3. Search
npx codebase-indexer search "authentication middleware"
4. Use with Claude Code or Codex
# Set up Claude Code integration (writes CLAUDE.md + .mcp.json)
npx codebase-indexer index ./your-project --setup-claude
# Set up Codex integration (writes AGENTS.md + .codex/config.toml)
npx codebase-indexer index ./your-project --setup-codex
# Set up both interactively
npx codebase-indexer index ./your-project --setup
# Global MCP setup (user-level config files)
npx codebase-indexer index ./your-project --setup --setup-globally
CLI Reference
Global Options
| Option | Default | Description |
|---|---|---|
--ollama-url <url> |
http://localhost:11434 |
Ollama API URL |
--qdrant-url <url> |
http://localhost:6333 |
Qdrant API URL |
--model <name> |
qwen3-embedding:0.6b |
Embedding model name |
--dim <number> |
512 |
Embedding vector dimension |
--dir <path> |
. |
Directory to index/watch |
--collection <name> |
codebase |
Qdrant collection name |
--no-watch |
— | Disable file watching in MCP mode |
Commands
init
Set up Qdrant (Docker) and check Ollama connection.
codebase-indexer init
- Creates
~/.codebase-indexer/docker-compose.yml - Starts Qdrant container (
qdrant/qdrant:latest) - Waits for health check (30s timeout)
- Verifies Ollama and auto-pulls the embedding model
index [directory]
Index a directory for semantic search.
codebase-indexer index ./my-project [options]
| Option | Description |
|---|---|
--setup |
Interactively choose setup targets (Claude/Codex) |
--setup-claude |
Write CLAUDE.md + .mcp.json for Claude Code |
--setup-codex |
Write AGENTS.md + .codex/config.toml for Codex |
--setup-globally |
Write MCP config to user-level files instead of project-local |
--force |
Re-index all files, ignoring cached hashes |
search <query>
Semantic search across the indexed codebase.
codebase-indexer search "database connection pooling" -k 5
codebase-indexer search "error handling" -l typescript
| Option | Default | Description |
|---|---|---|
-k, --top-k <number> |
10 |
Number of results |
-l, --language <lang> |
— | Filter by language |
status
Check health of Ollama and Qdrant, show index statistics.
codebase-indexer status
config
Interactively edit persistent settings.
codebase-indexer config
Opens an interactive menu to edit Ollama URL, Qdrant URL, embedding model, dimension, and collection name. Settings are saved to ~/.codebase-indexer/config.json and loaded by all commands automatically.
Default (no subcommand)
Start the MCP server over stdio.
codebase-indexer --dir ./my-project
MCP Server
When run without a subcommand (or via an MCP config), codebase-indexer starts as an MCP server using stdio JSON-RPC transport. It exposes five tools:
| Tool | Description |
|---|---|
search_code |
Semantic search with optional language and file_path_prefix filters |
index_file |
Index or re-index a single file |
index_directory |
Incrementally index an entire directory |
get_index_status |
Health check and index statistics |
delete_file |
Remove a file from the index |
Example .mcp.json
{
"mcpServers": {
"codebase-indexer": {
"command": "npx",
"args": ["codebase-indexer", "--dir", "/absolute/path/to/project"]
}
}
}
Example usage in Claude Code
search_code({ query: "retry logic with exponential backoff", top_k: 5 })
search_code({ query: "error handling", language: "typescript", file_path_prefix: "src/api/" })
index_file({ path: "src/new-module.ts" })
index_directory({})
get_index_status({})
delete_file({ path: "src/old-module.ts" })
Configuration
Settings are resolved in this order (last wins):
Hardcoded defaults → ~/.codebase-indexer/config.json → Environment variables → CLI flags
Environment Variables
| Variable | Maps to |
|---|---|
OLLAMA_URL |
--ollama-url |
QDRANT_URL |
--qdrant-url |
EMBEDDING_MODEL |
--model |
EMBEDDING_DIM |
--dim |
COLLECTION_NAME |
--collection |
Persistent Config
Run codebase-indexer config to interactively set values, or manually create ~/.codebase-indexer/config.json:
{
"ollamaUrl": "http://localhost:11434",
"qdrantUrl": "http://localhost:6333",
"model": "qwen3-embedding:0.6b",
"embeddingDim": 512,
"collectionName": "codebase"
}
Collection Name Auto-Derivation
When using the default collection name (codebase) and indexing a specific directory, the collection name is automatically derived from the directory name:
codebase-indexer index ./my-cool-project
# → collection: "codebase-my-cool-project"
Architecture
index.ts (CLI entry, commander.js)
→ cli/commands.ts (command handlers)
→ core/indexer.ts (orchestrator)
→ core/chunker.ts (symbol-based splitting, sliding window fallback)
→ core/embedder.ts (Ollama /api/embed client, MRL truncation + L2 normalize)
→ core/vectorstore.ts (Qdrant client, cosine similarity search)
→ mcp/server.ts (MCP stdio transport, 5 tools)
→ watcher/watcher.ts (chokidar, 500ms debounce, feeds into indexer)
Pipeline
Discover files → Chunk code → Embed via Ollama → Store in Qdrant
-
File discovery — uses
git ls-files(fast, respects.gitignore) with glob fallback. Filters by 54 code extensions, skips lock files, respects size limits (1 MB default). -
Chunking — dual strategy per file:
- Symbol-based: regex patterns detect functions, classes, interfaces, etc. for 18 languages
- Sliding window fallback: used when symbols cover <50% of the file. Default 1500 chars with 200-char overlap.
-
Embedding — batches of 16 chunks sent to Ollama's
/api/embedendpoint. Vectors are MRL-truncated to the target dimension and L2-normalized. Retry with exponential backoff (3 attempts). -
Storage — chunks upserted to Qdrant with deterministic IDs (
MD5(path:startLine:endLine)). Payload indices onfile_path,language, andchunk_typefor filtered search. Cosine similarity. -
Incremental indexing — each file's content hash is stored in the Qdrant payload. On re-index, unchanged files are skipped entirely.
Supported Languages (Symbol Detection)
| Language | Detected Symbols |
|---|---|
| TypeScript | functions, classes, interfaces, types, enums, arrow functions |
| JavaScript | functions, classes, arrow functions, module.exports |
| Python | functions, classes |
| Go | functions, types (struct, interface) |
| Rust | functions, structs, enums, traits, impl blocks |
| Java | classes, interfaces, methods |
| Kotlin | functions, classes, interfaces, objects |
| Ruby | functions, classes, modules |
| PHP | functions, classes, interfaces, traits |
| Swift | functions, classes, structs, protocols, enums |
| C# | methods, classes, interfaces |
| Scala | functions, classes, traits, objects |
| C | functions, structs, typedefs |
| C++ | functions, classes, structs, namespaces, templates |
| Elixir | functions, private functions, modules |
| Haskell | type signatures, data types, classes, instances |
| Dart | functions, classes, mixins |
| Zig | functions, const structs |
All other recognized file types fall back to sliding window chunking.
File Watcher
In MCP mode, the file watcher is enabled by default:
- Watches for file
add,change, andunlinkevents - 500ms debounce before processing
- 300ms write-finish stability detection
- Batch processing with retry (max 3 attempts, exponential backoff)
- Automatically re-indexes modified files and removes deleted ones
Disable with --no-watch.
Development
git clone https://github.com/ygtdgn/codebase-indexer.git
cd codebase-indexer
npm install
npm run dev # tsx watch mode (auto-rebuild on save)
Build
npm run build # tsc → dist/
Test
npm test # run once (vitest)
npm run test:watch # watch mode
Test coverage includes chunker (symbol detection, sliding window), embedder (truncation, normalization), file utilities (extension mapping, discovery), and hash functions (MD5, chunk IDs).
Project Structure
src/
├── index.ts # CLI entry point (commander.js)
├── config/
│ └── config.ts # Config interface, defaults, env vars, persistent config
├── cli/
│ ├── commands.ts # Command handlers (init, index, search, status, config)
│ ├── mascot.ts # Squirrel ASCII art with gradient colors
│ └── ui.ts # Spinners, progress formatting, result display
├── core/
│ ├── indexer.ts # Central orchestrator
│ ├── chunker.ts # Symbol-based + sliding window chunking
│ ├── embedder.ts # Ollama embedding client
│ └── vectorstore.ts # Qdrant CRUD operations
├── mcp/
│ └── server.ts # MCP stdio server (5 tools)
├── watcher/
│ └── watcher.ts # File change detection + auto-reindex
├── utils/
│ ├── files.ts # File discovery, language detection
│ ├── hash.ts # MD5, chunk IDs, index hashing
│ └── logger.ts # stderr logging (warn, error)
└── __tests__/
├── chunker.test.ts
├── embedder.test.ts
├── files.test.ts
└── hash.test.ts
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.