Token-Optimized MCP Server
Minimizes LLM token consumption via YAML serialization, HTML-to-Markdown distillation, and pre-flight token counting, with tools for web content extraction and metrics database querying.
README
Token-Optimized MCP Server
A high-performance Model Context Protocol (MCP) server built with Node.js and TypeScript. This server is purpose-built to minimize LLM token consumption through YAML serialization, structural HTML-to-Markdown distillation, and pre-flight token counting via the BPE tokenizer.
Table of Contents
- Overview
- Available Tools
- Prerequisites
- Installation
- Building
- Configuration
- Usage
- Project Structure
- Security
- License
Overview
Modern AI agents connected via MCP suffer from context window exhaustion when tool responses contain verbose JSON payloads or raw HTML. This server addresses the problem at the architecture layer:
| Optimization Strategy | Token Reduction |
|---|---|
| YAML serialization over JSON | ~40–48% |
| HTML → structured Markdown | ~60–80% |
| Pre-flight token gating | Prevents overflow |
All diagnostic logging is routed to stderr to preserve the JSON-RPC protocol integrity on stdout.
Available Tools
extract_web_content
Fetches a web page, strips HTML noise, and returns clean, semantically structured Markdown optimized for LLM consumption.
| Parameter | Type | Required | Description |
|---|---|---|---|
url |
string |
Yes | A valid URL to fetch and convert. |
Example input:
{
"url": "https://example.com"
}
Returns: Token-efficient Markdown with preserved heading hierarchy, links, and tables. Token count is logged to stderr.
query_metrics_database
Queries an internal metrics database and returns the results serialized in YAML format to reduce token overhead by approximately 45% compared to equivalent JSON.
| Parameter | Type | Required | Description |
|---|---|---|---|
query |
string |
Yes | A natural-language or structured query string. |
Example input:
{
"query": "Show CPU and memory usage for the last hour"
}
Returns: YAML-formatted metrics payload. If the response exceeds 8,000 tokens, a warning is emitted to stderr recommending semantic chunking.
Prerequisites
- Node.js v18.0.0 or higher
- npm v9+ (bundled with Node.js 18+)
Verify your installation:
node --version # Must be >= 18.0.0
npm --version
Installation
# Clone or navigate to the project directory
cd optimized-mcp-server
# Install all dependencies
npm install
Dependencies at a Glance
| Package | Purpose |
|---|---|
@modelcontextprotocol/sdk |
Official MCP server SDK |
zod |
Runtime input schema validation |
js-tiktoken |
BPE token counting (OpenAI compatible) |
node-html-markdown |
High-fidelity HTML → Markdown conversion |
@kreuzberg/html-to-markdown |
Native Rust-binding Markdown converter |
yaml |
JSON → YAML serialization |
Building
Compile the TypeScript source to JavaScript:
npm run build
The compiled output is written to the build/ directory.
Configuration
To connect this server to an MCP-compatible AI host, register it in the host's configuration file. Below are copy-pasteable templates for common hosts.
Claude Desktop
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"token-optimized-server": {
"command": "node",
"args": [
"/absolute/path/to/optimized-mcp-server/build/index.js"
]
}
}
}
Important: Replace
/absolute/path/to/with the actual absolute path on your system. On Windows, use double backslashes (\\) or forward slashes (/) in JSON strings.
After saving, fully quit and restart Claude Desktop to re-initialize the JSON-RPC handshake.
VS Code / Antigravity IDE
Add the following to your MCP configuration file (typically mcp_config.json in your editor's settings directory):
{
"mcpServers": {
"token-optimized-server": {
"command": "node",
"args": [
"/absolute/path/to/optimized-mcp-server/build/index.js"
]
}
}
}
Windows example:
"args": [ "C:\\Users\\YourName\\projects\\optimized-mcp-server\\build\\index.js" ]
Usage
Development Mode
Run the server directly from TypeScript source using tsx (no build step required):
npm run dev
Production Mode
Build first, then start the compiled server:
npm run build
npm start
MCP Inspector
The MCP Inspector provides a browser-based UI for testing tools, simulating LLM requests, and inspecting JSON-RPC messages — no API keys required.
npx @modelcontextprotocol/inspector node build/index.js
This launches a local proxy and opens the Inspector UI in your default browser. Use it to:
- Verify the protocol handshake completes successfully.
- Execute
extract_web_contentwith a test URL. - Execute
query_metrics_databaseand confirm YAML output. - Monitor
stderrlogs for token counts and threshold warnings.
Project Structure
optimized-mcp-server/
├── src/
│ └── index.ts # Core server implementation
├── build/ # Compiled JavaScript output (generated)
├── node_modules/ # Dependencies (generated)
├── package.json # Project metadata and scripts
├── tsconfig.json # TypeScript compiler configuration
├── .gitignore # Git exclusion rules
├── README.md # This file
└── SECURITY.md # Security guidelines and best practices
License
ISC
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.