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bevanding/signaldaemon

by Various

Narrative & signal intelligence MCP server for AI agents (crypto/AI/macro). Remote MCP at api.signaldaemon.com/mcp.

B

MCP

bevanding/signaldaemon

Added 23 June 2026

Overview

A Model Context Protocol (MCP) server that provides AI agents with narrative and signal intelligence data focused on crypto, AI, and macro trends. It exposes a remote MCP endpoint at api.signaldaemon.com/mcp for agent integration.

Best for

Best for
Developers building AI agents that need curated signal and narrative intelligence in crypto and macro markets

Use cases

  • Feeding AI agents with real-time narrative and signal data for trading analysis
  • Enabling agent-driven macro trend monitoring across crypto and AI sectors
  • Integrating signal intelligence into automated decision-making pipelines

Notes

A Model Context Protocol (MCP) server that provides AI agents with narrative and signal intelligence data focused on crypto, AI, and macro trends. It exposes a remote MCP endpoint at api.signaldaemon.com/mcp for agent integration.

0 stars on GitHub. Last updated 2026-06-18. Licensed MIT.

Use cases

  • Feeding AI agents with real-time narrative and signal data for trading analysis
  • Enabling agent-driven macro trend monitoring across crypto and AI sectors
  • Integrating signal intelligence into automated decision-making pipelines

Pros

  • Remote MCP endpoint reduces local setup overhead for agents
  • Specialized data focus on high-interest domains like crypto and AI
  • Python-based implementation aligns with common agent toolchains

Cons

  • Zero stars and no community traction suggests limited reliability
  • No local deployment option may increase latency or dependency on external service
  • Narrow domain scope limits general-purpose agent use cases

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

Pros

  • Remote MCP endpoint reduces local setup overhead for agents
  • Specialized data focus on high-interest domains like crypto and AI
  • Python-based implementation aligns with common agent toolchains

Cons

  • Zero stars and no community traction suggests limited reliability
  • No local deployment option may increase latency or dependency on external service
  • Narrow domain scope limits general-purpose agent use cases
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