context-saver

MCP proxy that reduces context usage through semantic tool routing.

The Problem

MCP tools consume massive amounts of context tokens before conversations even start:

Server	Tools	Tokens
Notion	14	~16,500
Google Drive	99	~18,000
Chrome DevTools	29	~5,800
Total	142	~40,300

That's 40k tokens gone before you ask a single question.

The Solution

context-saver sits between Claude Code and your MCP servers, using vector embeddings to surface only relevant tools on-demand.

Claude Code ──► context-saver ──► Backend MCP Servers
                    │
                    ▼
                LanceDB
             (tool embeddings)

Results:

Mode	Initial Tokens	Tools Available
Before	~40,000	All 142
Standard	~8,000	All 142
Lite	~500	All 142 (on-demand)

Quick Start

1. Install

npm install -g context-saver

2. Create Config

Create ~/.context-saver/config.json:

{
  "embedding": {
    "provider": "openai",
    "model": "text-embedding-3-small"
  },
  "discovery": {
    "liteMode": true
  },
  "backends": {
    "filesystem": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/user"]
    }
  }
}

3. Set API Key

export OPENAI_API_KEY="sk-..."

4. Add to Claude Code

Add to your Claude Code MCP settings (~/.claude/settings.json):

{
  "mcpServers": {
    "context-saver": {
      "command": "npx",
      "args": ["context-saver"]
    }
  }
}

5. Use It

In Claude Code, use discover_tools to find what you need:

> discover_tools("update notion pages")

Found 3 relevant tools:

1. notion-update-page (notion)
   Update a Notion page's content
   Parameters: page_id*, content*
   Relevance: 94%

2. notion-fetch (notion)
   Fetch a Notion page by ID
   Parameters: page_id*
   Relevance: 87%
...

Configuration

Full Example

{
  "version": "1.0",

  "embedding": {
    "provider": "openai",
    "model": "text-embedding-3-small",
    "dimensions": 1536,
    "apiKey": "${OPENAI_API_KEY}"
  },

  "storage": {
    "path": "~/.context-saver/lancedb",
    "reindexOnStart": false
  },

  "discovery": {
    "defaultTopK": 5,
    "minSimilarity": 0.3,
    "liteMode": true
  },

  "backends": {
    "notion": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@anthropic/mcp-server-notion"],
      "env": {
        "NOTION_API_KEY": "${NOTION_API_KEY}"
      }
    },
    "google-drive": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@anthropic/mcp-server-google-drive"]
    }
  }
}

Options

embedding

Option	Default	Description
provider	"openai"	Embedding provider (see below)
model	varies	Model name
dimensions	varies	Embedding dimensions
apiKey	env var	API key (supports env var syntax)

Supported Providers:

Provider	Model	Dimensions	API Key
openai	text-embedding-3-small	1536	OPENAI_API_KEY
gemini	text-embedding-004	768	GOOGLE_API_KEY
cohere	embed-english-v3.0	1024	COHERE_API_KEY
ollama	nomic-embed-text	768	None (local)
local	Xenova/all-MiniLM-L6-v2	384	None (local)

Local embeddings (no API key needed):

{
  "embedding": {
    "provider": "local",
    "model": "Xenova/all-MiniLM-L6-v2",
    "dimensions": 384
  }
}

discovery

Option	Default	Description
defaultTopK	5	Default number of tools returned
minSimilarity	0.3	Minimum similarity threshold (0-1)
liteMode	false	Maximum savings: only expose discover_tools initially

storage

Option	Default	Description
path	~/.context-saver/lancedb	LanceDB storage location
reindexOnStart	false	Force reindex on every startup

backends

Each backend can be:

STDIO (local process):

{
  "type": "stdio",
  "command": "npx",
  "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path"],
  "env": { "KEY": "value" }
}

Remote (HTTP - coming soon):

{
  "type": "remote",
  "url": "https://mcp.example.com",
  "headers": { "Authorization": "Bearer ..." }
}

Built-in Tools

context-saver exposes six meta-tools:

discover_tools

Semantic search for relevant tools.

discover_tools({ query: "search google drive", limit: 5 })

list_all_tools

List all available tools grouped by server.

list_all_tools()

tool_info

Get detailed information about a specific tool including full parameter schema.

tool_info({ tool_name: "notion-update-page" })

similar_tools

Find tools similar to one you already know.

similar_tools({ tool_name: "read_file", limit: 5 })

tools_by_category

List tools filtered by category.

tools_by_category({ category: "filesystem" })

Categories: filesystem, documents, spreadsheets, presentations, images, calendar, messaging, database, browser, version-control

server_stats

Get statistics about context-saver including connected backends, indexed tools, and usage stats.

server_stats()

Lite Mode

For maximum token savings, enable liteMode:

{
  "discovery": {
    "liteMode": true
  }
}

In lite mode:

• Only discover_tools and list_all_tools are exposed initially (~500 tokens)
• All backend tools are still available and routed correctly
• Use discover_tools to find what you need

How It Works

1. Startup: Connects to all backend MCP servers and indexes their tools
2. Indexing: Creates embeddings for each tool using OpenAI
3. Storage: Stores embeddings in LanceDB for fast vector search
4. Discovery: When you call discover_tools, performs cosine similarity search
5. Routing: Tool calls are routed to the correct backend server

Development

git clone https://github.com/msuther898/context-saver.git
cd context-saver
npm install
npm run build
npm start

Project Structure

src/
├── index.ts              # Entry point
├── server.ts             # MCP server + handlers
├── client-pool.ts        # Backend connections
├── config/               # Config types + loader
├── discovery/
│   ├── indexer.ts        # Tool indexing with synonyms
│   └── search.ts         # Vector search + re-ranking
├── embeddings/
│   ├── index.ts          # Provider factory
│   ├── openai.ts         # OpenAI embeddings
│   ├── gemini.ts         # Google Gemini embeddings
│   ├── cohere.ts         # Cohere embeddings
│   ├── ollama.ts         # Ollama local embeddings
│   └── local.ts          # Transformers.js embeddings
└── storage/
    └── lancedb.ts        # LanceDB vector storage

Roadmap

• [x] Ollama embeddings support
• [x] Local embeddings (transformers.js)
• [x] Gemini embeddings support
• [x] Cohere embeddings support
• [x] Usage tracking and popularity boosting
• [x] Re-ranking with multiple signals
• [x] Category-based tool filtering
• [ ] Remote HTTP backend support
• [ ] Tool result caching
• [ ] Persistent usage stats

License

MIT

Credits

Built by @msuther898 with Claude.