washedmcp 0.1.0

Token-optimized semantic code search MCP server for AI coding assistants

WashedMCP — Token-Optimized Semantic Code Search

An MCP (Model Context Protocol) server that provides token-efficient semantic code search with automatic context expansion for AI coding assistants.

The Problem

When AI assistants search codebases, they get isolated results without context:

• Need multiple searches to understand call chains
• Waste tokens on redundant lookups
• Lose context between tool calls

The Solution

WashedMCP returns comprehensive context in a single search:

Query: "user validation logic"

FOUND: validate() in src/auth.js:42 (82% match)

CODE:
  function validate(data) {
    if (!checkEmail(data.email)) return false;
    if (!checkPassword(data.password)) return false;
    return sanitize(data);
  }

CALLS: checkEmail, checkPassword, sanitize
CALLED BY: processUser, createUser
SAME FILE: [sanitize, normalizeInput, validateSchema]

One search → full context → immediate action.

Features

• Semantic Search — Find code by meaning, not just keywords
• Context Expansion — Automatically include callers/callees
• Code Graph — Track function relationships (calls, called_by)
• TOON Format — Token-Optimized Object Notation (~30-40% fewer tokens than JSON)
• Multi-Language — Python, JavaScript, TypeScript, JSX, TSX

Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Index Your Codebase

python src/cli.py index /path/to/your/codebase

3. Search

python src/cli.py search "authentication flow"

4. Use with Claude Code

Add to .mcp.json:

{
  "mcpServers": {
    "washedmcp": {
      "command": "python3",
      "args": ["src/mcp_server.py"]
    }
  }
}

MCP Tools

Tool	Description
index_codebase	Index a codebase for semantic search
search_code	Search with context expansion (depth parameter)
get_index_status	Check if codebase is indexed

How It Works

┌─────────────────────────────────────────────────┐
│               CONTEXT EXPANSION                  │
├─────────────────────────────────────────────────┤
│                                                  │
│  Query: "validation failing"                     │
│              │                                   │
│              ▼                                   │
│  ┌────────────────────────────┐                 │
│  │  1. Semantic Search        │                 │
│  │     (embeddings + cosine)  │                 │
│  └────────────────────────────┘                 │
│              │                                   │
│              ▼                                   │
│  ┌────────────────────────────┐                 │
│  │  2. Context Expansion      │                 │
│  │     • CALLS: [...]         │                 │
│  │     • CALLED BY: [...]     │                 │
│  │     • SAME FILE: [...]     │                 │
│  └────────────────────────────┘                 │
│              │                                   │
│              ▼                                   │
│  ┌────────────────────────────┐                 │
│  │  3. TOON Output            │                 │
│  │     (token-efficient)      │                 │
│  └────────────────────────────┘                 │
│                                                  │
└─────────────────────────────────────────────────┘

Tech Stack

• Parsing: tree-sitter (multi-language AST extraction)
• Embeddings: sentence-transformers/all-MiniLM-L6-v2
• Vector DB: ChromaDB (persistent, cosine similarity)
• MCP: fastmcp
• Summarization: Google Generative AI (optional)

Project Structure

washedmcp/
├── src/
│   ├── parser.py         # AST parsing + call extraction
│   ├── embedder.py       # Embedding generation
│   ├── database.py       # ChromaDB + relationships
│   ├── indexer.py        # Indexing orchestration
│   ├── searcher.py       # Search + context expansion
│   ├── summarizer.py     # Function summarization
│   ├── toon_formatter.py # TOON output format
│   ├── mcp_server.py     # MCP server
│   └── cli.py            # CLI interface
├── tests/                 # Test codebase samples
├── docs/                  # Design documents
└── requirements.txt       # Dependencies

Context Expansion Depth

Control how many hops of relationships to include:

• depth=1 (default): Direct callers + callees
• depth=2: Include callers of callers (for debugging chains)

# MCP tool call
search_code(query="validation", depth=2)

License

MIT