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msuther898

context-saver

by msuther898
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

context-saver

CI npm version

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.

Related MCP server: MCP Vector Proxy

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

  • Ollama embeddings support

  • Local embeddings (transformers.js)

  • Gemini embeddings support

  • Cohere embeddings support

  • Usage tracking and popularity boosting

  • Re-ranking with multiple signals

  • Category-based tool filtering

  • Remote HTTP backend support

  • Tool result caching

  • Persistent usage stats

License

MIT

Credits

Built by @msuther898 with Claude.

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