Glancey

This project has been sunset. Improvements in Claude's underlying models have made the semantic code search capabilities that Glancey provided no longer necessary. Claude Code now handles codebase understanding well enough on its own that an external indexing layer adds little value. The package will remain available on npm for existing users, but no further development or maintenance is planned. Thank you to everyone who tried it out!

An MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.

Features

• Semantic Code Search: Natural language queries locate relevant code across your entire codebase
• Token Savings: Dramatically reduces context usage by returning only relevant code chunks
• Multiple Embedding Backends: Google Gemini (free) or Ollama (local)
• LanceDB Vector Storage: Fast, efficient vector search with hybrid BM25 + dense matching
• MCP Compatible: Works with Claude Code, Cursor, and other MCP-compatible tools
• Web Dashboard: Real-time monitoring of index status, token savings, and usage statistics
• Beads Integration: Shows issue tracker data if your project uses beads

Installation

Quick Install (Recommended)

Add Glancey to Claude Code:

claude mcp add --scope user --transport stdio glancey -- npx -y glancey

Restart Claude Code to start using semantic search.

Global Install (Alternative)

For faster startup (no npm check on each run):

npm install -g glancey

This automatically registers Glancey with Claude Code. Update manually with npm update -g glancey.

Manual Registration

If automatic registration didn't work, manually add to Claude Code:

claude mcp add --scope user --transport stdio glancey -- npx -y glancey@latest

Verify Installation

In Claude Code, run /mcp to see Glancey in the list of MCP servers.

Initialize Your Project (Recommended)

After installing Glancey, run /init_project in Claude Code to set up your project:

/init_project

This creates:

• CLAUDE.md - Instructions for AI agents on how to use Glancey tools
• Post-commit hook - Warns when commits bypass the commit tool
• Slash commands - /glancey, /dashboard, /agents, and /init-project

The hook is installed in .husky/ if you use Husky, otherwise in .git/hooks/.

Tip: If your agent isn't using Glancey tools (falling back to grep/find instead), run /glancey to remind it. When launching new agents (subagents, worktrees, etc.), tell them to run /glancey before starting their task so they know to use Glancey's semantic search instead of manual exploration.

Slash Commands

Glancey installs the following slash commands into .claude/commands/ for use in Claude Code:

Command	Description
/glancey	Show glancey tool usage tips and quick reference. Use this to remind agents to use semantic search instead of grep/find.
/dashboard	Open the glancey web dashboard in the browser for real-time monitoring of index status, token savings, and usage.
/agents	Launch 1-10 parallel Claude Code agents, each working autonomously on a beads task in its own git worktree. Supports --auto-merge, --count N, --model MODEL, and --budget N flags.
/init-project	Initialize glancey in the current project — sets up CLAUDE.md, post-commit hook, and slash commands.

Project-Level Installation

For project-specific MCP configuration, add a .mcp.json to your project root:

{
  "mcpServers": {
    "glancey": {
      "command": "npx",
      "args": ["-y", "glancey@latest"]
    }
  }
}

Project Configuration

Create a .glancey.json file in your project root to customize indexing behavior. All options are optional - Glancey works out of the box with sensible defaults.

Minimal Configuration

For most projects, you only need to specify what to include:

{
  "patterns": ["**/*.ts", "**/*.js"],
  "instructions": "This is a TypeScript monorepo. Use semantic search to find relevant utilities."
}

Full Configuration Example

{
  "patterns": ["**/*.ts", "**/*.tsx", "**/*.js", "**/*.jsx"],
  "excludePatterns": ["**/node_modules/**", "**/dist/**", "**/*.test.ts"],
  "embedding": {
    "backend": "gemini"
  },
  "chunking": {
    "maxLines": 100,
    "overlap": 20
  },
  "search": {
    "semanticWeight": 0.7,
    "keywordWeight": 0.3
  },
  "dashboard": {
    "enabled": true,
    "port": 24300,
    "openBrowser": true
  },
  "instructions": "Project-specific instructions for AI agents working with this codebase."
}

Configuration Options Reference

Option	Description	Default
patterns	Glob patterns for files to index	["**/*.ts", "**/*.tsx", "**/*.js", "**/*.jsx", "**/*.py", "**/*.go", "**/*.rs", "**/*.java", "**/*.rb", "**/*.php", "**/*.c", "**/*.cpp", "**/*.h", "**/*.hpp", "**/*.cs", "**/*.swift", "**/*.kt"]
excludePatterns	Glob patterns for files to exclude	["**/node_modules/**", "**/dist/**", "**/.git/**", "**/build/**", "**/target/**", "**/__pycache__/**", "**/venv/**", "**/.venv/**", "**/vendor/**", "**/*.min.js", "**/*.min.css"]
embedding.backend	Embedding provider: "gemini" or "ollama"	Auto-detect based on available API keys
embedding.model	Override the default embedding model	Backend default
embedding.ollamaConcurrency	Max concurrent Ollama requests (1-200)	100
indexing.batchSize	Texts per embedding batch request (1-1000)	200
chunking.maxLines	Maximum lines per chunk	100
chunking.overlap	Overlapping lines between chunks for context continuity	20
search.semanticWeight	Weight for semantic (vector) similarity (0-1)	0.7
search.keywordWeight	Weight for BM25 keyword matching (0-1)	0.3
dashboard.enabled	Enable the web dashboard	true
dashboard.port	Port for the dashboard server	24300
dashboard.openBrowser	Auto-open browser when dashboard starts	true
instructions	Project-specific instructions returned by get_project_instructions	None

Default Behavior

Without a .glancey.json file, Glancey will:

• Index common source code files (TypeScript, JavaScript, Python, Go, Rust, Java, Ruby, PHP, C/C++, C#, Swift, Kotlin)
• Exclude build artifacts, dependencies, and generated files
• Use Gemini embeddings if GEMINI_API_KEY is set, otherwise use local Ollama with qwen3-embedding:0.6b
• Split code into 100-line chunks with 20-line overlap
• Use hybrid search with 70% semantic / 30% keyword weighting
• Start the dashboard on port 24300

Environment Variables

Set these environment variables to configure embedding backends:

Variable	Description	Default
GEMINI_API_KEY	Google Gemini API key for cloud embeddings (free tier available)	None
OLLAMA_URL	Custom Ollama server URL for local embeddings	http://localhost:11434
GLANCEY_PROJECT	Override the project path to index	Current working directory

Backend Selection Priority:

1. If embedding.backend is set in config, use that backend
2. If GEMINI_API_KEY is set, use Gemini
3. Fall back to Ollama (must be running locally)

How Glancey Saves Tokens

AI coding agents typically need to read entire files to understand your codebase, which consumes significant context tokens. Glancey dramatically reduces token usage by:

Without Glancey	With Glancey	Savings
Read 5-10 files to find auth code (~5000 lines)	search_code returns 3 chunks (~150 lines)	~97%
Read entire file to understand structure	get_symbols_overview returns compact list	~80-90%
Explore many files to understand codebase	summarize_codebase + list_concepts	~95%
Read and compare files for duplicates	search_similar returns targeted results	~90%

Token Savings Dashboard

The web dashboard displays real-time token savings statistics:

• Estimated Tokens Saved: Total tokens avoided by using semantic search
• Efficiency: Percentage of potential tokens saved
• Files Not Read: Count of files skipped due to targeted search
• Operations Tracked: Number of search operations contributing to savings

How It Works

1. Chunking: Your codebase is split into semantic chunks (functions, classes, etc.)
2. Embedding: Each chunk is converted to a vector embedding
3. Search: Queries find only the most relevant chunks, not entire files
4. Return: Only the matching chunks are sent to the AI, saving context tokens

Architecture

┌─────────────────────────────────────────────────────────────┐
│                    MCP Server (index.ts)                    │
│         Exposes tools: index_codebase, search_code          │
└─────────────────┬───────────────────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────────────────┐
│                  CodeIndexer (indexer.ts)                   │
│  - AST-aware chunking for supported languages               │
│  - Incremental indexing (only re-index changed files)       │
│  - Hybrid search (semantic + keyword scoring)               │
└─────────────────┬───────────────────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────────────────┐
│              Embedding Backends (embeddings/)               │
│            Gemini  │  Ollama (local)                        │
└─────────────────┬───────────────────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────────────────┐
│                   LanceDB Vector Store                      │
│           Stored in .glancey/ directory               │
└─────────────────────────────────────────────────────────────┘

Embedding Backend Setup

Glancey automatically selects the best available backend (in priority order):

1. Google Gemini (if GEMINI_API_KEY is set, free tier available)

   export GEMINI_API_KEY=AIza...

2. Ollama (recommended for most users - free, local, no rate limits)

Ollama Setup (Recommended)

Ollama provides free, local embeddings with no API rate limits. Perfect for indexing large codebases.

Requirements: Ollama 0.2.0 or newer (for batch embedding API)

1. Install Ollama from ollama.com

2. Verify version (must be 0.2.0+):

   ollama --version

3. Pull the embedding model:

   ollama pull qwen3-embedding:0.6b

4. Verify it's working:

   ollama run qwen3-embedding:0.6b "test"

That's it! Glancey will automatically use Ollama when no Gemini API key is set.

Model Options

Model	Size	Quality	Best For
qwen3-embedding:0.6b	639MB	Good	Most users (default)
qwen3-embedding:4b	2.5GB	Better	Users with 16GB+ RAM
qwen3-embedding:8b	4.7GB	Best	Users with 32GB+ RAM

To use a different model, add to your .glancey.json:

{
  "embedding": {
    "backend": "ollama",
    "model": "qwen3-embedding:4b"
  }
}

See Project Configuration for all configuration options including how to specify a backend.

Usage

Once installed, you'll have access to these tools:

index_codebase

Index your codebase for semantic search:

> index_codebase
Indexed 150 files, created 800 chunks.

With custom patterns:

> index_codebase(patterns: ["**/*.py"], excludePatterns: ["**/tests/**"])

search_code

Search using natural language:

> search_code(query: "authentication middleware")

## Result 1: src/middleware/auth.ts:1-50
...

get_index_status

Check index status:

> get_index_status
{
  "indexed": true,
  "fileCount": 150,
  "chunkCount": 800,
  "lastUpdated": "2024-12-27T12:00:00Z"
}

clear_index

Clear the index:

> clear_index
Index cleared.

get_project_instructions

Get project-specific instructions from the config:

> get_project_instructions
Use semantic search for exploring this codebase. Always run tests before committing.

Dashboard

Glancey includes a web dashboard for monitoring index status and usage.

Accessing the Dashboard

The dashboard starts automatically when the MCP server runs and is available at:

http://127.0.0.1:24300

The browser opens automatically on startup (configurable).

Dashboard Features

• Index Status: Files indexed, chunks created, last updated time
• Embedding Backend: Current backend and index path
• Configuration: Project path, chunk settings, search weights
• File Patterns: Include/exclude patterns being used
• Command Usage: Real-time chart of MCP tool usage (using charts.css)
• Beads Integration: Issue tracker status and ready tasks (if beads is configured)

Dashboard Configuration

Configure the dashboard via the dashboard options in .glancey.json. See Configuration Options Reference for details.

How It Works

1. Indexing: Code files are chunked into ~100-line segments with overlap
2. Embedding: Each chunk is converted to a vector using your chosen backend
3. Storage: Vectors are stored in LanceDB (.glancey/ directory)
4. Search: Natural language queries are embedded and matched against stored vectors

Supported Languages

TypeScript, JavaScript, Python, Go, Rust, Java, Ruby, PHP, C/C++, C#, Swift, Kotlin, and more.

Troubleshooting

"No embedding backend available"

This error means no API keys are set and Ollama is not running/accessible.

Solutions:

1. Set up Ollama (recommended):

   # Install from https://ollama.com, then:
   ollama pull qwen3-embedding:0.6b

2. Or set a Gemini API key: export GEMINI_API_KEY=AIza...

"Embedding dimension mismatch"

This occurs when switching between embedding backends (e.g., from Gemini to Ollama). Each backend produces different vector dimensions.

Solution: Force a full reindex:

> index_codebase(forceReindex: true)

Slow Indexing

Large codebases may take time to index initially.

Tips:

1. Use excludePatterns to skip unnecessary directories (tests, generated code)
2. Ollama is faster for local use but requires more resources
3. Subsequent runs use incremental indexing (only changed files)

Index Corruption

If you encounter strange search results or errors:

Solution: Clear and rebuild the index:

> clear_index
> index_codebase

Or manually delete the .glancey/ directory and re-index.

License

MIT - See LICENSE for details.

Contributing

Contributions welcome! Please read our Contributing Guide before submitting PRs.

Related Projects

• claude-context - Similar tool using Zilliz Cloud
• Serena - Symbol-level code navigation

Credits

Built with:

• LanceDB - Vector database
• MCP SDK - Model Context Protocol
• Google AI Studio - Gemini Embedding API

Inspired by:

• Serena by Oraios - Symbol-level code navigation and editing