@claymore-dev/anvil

MCP server that makes your project docs searchable by AI. Point it at a folder of markdown files, and it chunks, embeds, and serves them over the Model Context Protocol — so your AI assistant can actually find relevant documentation.

Quickstart

npm install -g @claymore-dev/anvil
cd your-project
anvil serve --docs ./docs

That's it. Anvil indexes your markdown, starts an MCP server on stdio, and exposes 5 tools your AI can call.

What It Does

1. Scans a directory for .md files
2. Chunks them by heading structure (not fixed-size blocks)
3. Embeds each chunk using a local transformer model (no API keys needed)
4. Stores chunks + vectors in a local SQLite database with sqlite-vss
5. Watches for file changes and re-indexes automatically
6. Serves semantic search + page/section retrieval over MCP (stdio transport)

Installation

npm install -g @claymore-dev/anvil

Requires Node.js 18+. The embedding model (~80 MB) downloads automatically on first run.

Configuration

Create anvil.config.json in your project root (or run anvil init):

{
  "docs": "./docs",
  "db": "./.anvil/index.db",
  "embedding": {
    "provider": "local",
    "model": "all-MiniLM-L6-v2"
  },
  "chunking": {
    "maxChunkSize": 6000,
    "minChunkSize": 200,
    "mergeShort": true
  },
  "watch": true,
  "logLevel": "info"
}

All fields are optional — defaults are shown above.

Field	Description
docs	Path to markdown directory (default: ./)
db	SQLite database path (default: ./.anvil/index.db)
embedding.provider	"local" (default) or "openai"
embedding.model	HuggingFace model name (default: all-MiniLM-L6-v2)
chunking.maxChunkSize	Max characters per chunk before splitting (default: 6000)
chunking.minChunkSize	Short chunks below this merge into parent (default: 200)
chunking.mergeShort	Enable short-chunk merging (default: true)
watch	Watch for file changes (default: true)
logLevel	silent, error, warn, info, or debug

CLI flags override config file values. Config file overrides defaults.

MCP Client Setup

Cursor

In .cursor/mcp.json:

{
  "mcpServers": {
    "anvil": {
      "command": "anvil",
      "args": ["serve", "--docs", "./docs"]
    }
  }
}

Claude Desktop

In ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "anvil": {
      "command": "anvil",
      "args": ["serve", "--docs", "/absolute/path/to/your/docs"]
    }
  }
}

OpenClaw / mcporter

{
  "mcpServers": {
    "anvil": {
      "command": "anvil",
      "args": ["serve", "--docs", "/absolute/path/to/your/docs"]
    }
  }
}

CLI Reference

anvil serve (default)

Start the MCP server over stdio.

anvil serve [options]

Flag	Description
-d, --docs <path>	Docs directory (default: ./)
--db <path>	Database path
-c, --config <path>	Config file path (default: ./anvil.config.json)
--no-config	Ignore config file
--no-watch	Disable file watcher
--max-chunk-size <n>	Max chunk size in characters
--min-chunk-size <n>	Min chunk size before merge
--embedding-provider <p>	local or openai
--log-level <level>	silent, error, warn, info, debug
-v, --version	Print version

anvil index

One-shot indexing without starting the MCP server.

anvil index [options]

Same flags as serve, plus:

Flag	Description
--force	Clear index and re-embed everything

anvil init

Create anvil.config.json interactively.

anvil init [--yes]

Flag	Description
--yes	Skip prompts, use all defaults

MCP Tools

Anvil exposes 5 tools to connected MCP clients:

Tool	Description
search_docs	Semantic search across all indexed docs
get_page	Retrieve full page content by file path
get_section	Retrieve a specific section by heading path
list_pages	List all indexed pages with metadata
get_status	Server health, index state, and version info

Architecture Overview

┌─────────────┐     ┌──────────┐     ┌──────────┐
│  .md files  │────▶│ Chunker  │────▶│ Embedder │
└─────────────┘     └──────────┘     └──────────┘
                         │                 │
                         ▼                 ▼
                    ┌─────────────────────────┐
                    │   SQLite + sqlite-vss   │
                    └─────────────────────────┘
                              │
                              ▼
                    ┌─────────────────────┐
                    │  MCP Server (stdio) │
                    └─────────────────────┘
                              │
                    ┌─────────────────────┐
                    │  AI Client (Cursor, │
                    │  Claude Desktop...) │
                    └─────────────────────┘

• Chunker (src/chunker.ts) — Splits markdown by heading structure using remark AST. Handles frontmatter, duplicate headings, short-section merging, and long-section splitting.
• Embedder (src/embedder.ts) — Generates 384-dim vectors using all-MiniLM-L6-v2 via @huggingface/transformers. Runs entirely local, no API keys.
• Database (src/db.ts) — SQLite with sqlite-vss for vector similarity search. WAL mode for concurrent reads.
• Indexer (src/indexer.ts) — Diffing engine that only re-embeds changed chunks. Detects model changes and triggers full re-embed.
• Watcher (src/watcher.ts) — Chokidar-based file watcher with debouncing. Re-indexes on add/change/delete.
• Query Engine (src/query.ts) — Vector search, page retrieval, section retrieval, and page listing.
• Server (src/server.ts) — Orchestrates startup: scan → index → watch → MCP.
• Tools (src/tools/index.ts) — MCP tool definitions and handlers.

Contributing

1. Fork the repo
2. Create a feature branch: git checkout -b my-feature
3. Run tests: npm test
4. Submit a PR

License

MIT © Dan Hannah