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0xDegenMo/lighter-mcp

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0xDegenMo/lighter-mcp — indexed from awesome-mcp-servers-punkpeye

MCP

0xDegenMo/lighter-mcp

Added 1 June 2026

Overview

A lightweight Python implementation of a Model Context Protocol (MCP) server. It provides a minimal interface for serving context to AI models, sourced from the awesome-mcp-servers collection.

Best for

Best for
Developers exploring the Model Context Protocol who want a simple starting point

Use cases

  • Integrating custom context into AI model workflows
  • Prototyping MCP-based applications
  • Learning the Model Context Protocol with a simple codebase

How to use

Install

pip install 0xdegenmo-lighter-mcp

Tools exposed

  • LIGHTER_URL
  • LIGHTER_ACCOUNT_INDEX
  • LIGHTER_PRIVATE_KEY
  • LIGHTER_API_KEY_INDEX
  • LIGHTER_READ_ONLY
  • LIGHTER_MAX_QUOTE_USD
  • LIGHTER_MAX_SLIPPAGE
  • LIGHTER_ALLOWED_SYMBOLS
  • LIGHTER_NO_INTEGRATOR
  • LIGHTER_INTEGRATOR_ACCOUNT_INDEX
  • LIGHTER_INTEGRATOR_MAKER_FEE
  • LIGHTER_INTEGRATOR_TAKER_FEE
  • LIGHTER_MCP_LOG_LEVEL

Tested with

Claude Desktop, Cursor

Notes

A lightweight Python implementation of a Model Context Protocol (MCP) server. It provides a minimal interface for serving context to AI models, sourced from the awesome-mcp-servers collection.

1 stars on GitHub. Last updated 2026-05-28. Licensed MIT.

Use cases

  • Integrating custom context into AI model workflows
  • Prototyping MCP-based applications
  • Learning the Model Context Protocol with a simple codebase

Pros

  • Lightweight and minimal Python codebase
  • Easy to understand and modify for experimentation

Cons

  • Very early stage with only 1 star and limited community
  • Minimal documentation and usage examples
  • Unclear production readiness or ongoing maintenance

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Lightweight and minimal Python codebase
  • Easy to understand and modify for experimentation

Cons

  • Very early stage with only 1 star and limited community
  • Minimal documentation and usage examples
  • Unclear production readiness or ongoing maintenance

Open-source & AI alternatives

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