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Chronolapse411/sicarius-guard

by Various

Solana Token Safety Oracle for AI Agents & Trading Bots — byte-level SPL analysis, honeypot detection, Birdeye enrichment, MCP server

C

MCP

Chronolapse411/sicarius-guard

Added 1 June 2026

#ai-agents #birdeye #blockchain #cryptocurrency #defi #helius #honeypot-detection #mcp-server

Overview

Sicarius Guard is a TypeScript library that provides a Solana token safety oracle for AI agents and trading bots. It performs byte-level SPL analysis and honeypot detection, enriched with Birdeye data, and can run as an MCP server.

Best for

Best for
Developers building Solana trading bots or AI agents that need local token safety verification

Use cases

  • Integrating token safety checks into autonomous trading bots on Solana
  • Adding honeypot detection to AI agent workflows
  • Enriching on-chain token analysis with Birdeye metadata

Notes

Sicarius Guard is a TypeScript library that provides a Solana token safety oracle for AI agents and trading bots. It performs byte-level SPL analysis and honeypot detection, enriched with Birdeye data, and can run as an MCP server.

1 stars on GitHub. Last updated 2026-05-20. Licensed MIT.

Use cases

  • Integrating token safety checks into autonomous trading bots on Solana
  • Adding honeypot detection to AI agent workflows
  • Enriching on-chain token analysis with Birdeye metadata

Pros

  • Provides deep byte-level SPL analysis for robust token inspection
  • Includes honeypot detection to prevent common token scams
  • Works as an MCP server for flexible agent integration

Cons

  • Low community traction with only 1 star on GitHub
  • Limited documentation and support available
  • Depends on Birdeye API which may have usage costs or rate limits

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

Pros

  • Provides deep byte-level SPL analysis for robust token inspection
  • Includes honeypot detection to prevent common token scams
  • Works as an MCP server for flexible agent integration

Cons

  • Low community traction with only 1 star on GitHub
  • Limited documentation and support available
  • Depends on Birdeye API which may have usage costs or rate limits