How do I use BlackSwan MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@BlackSwan MCP Server What does Flare say about current crypto risk?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

BlackSwan MCP Server

by blackswanwtf

Overview Schema Related Servers Score Discussions

TypeScript

Remote

BlackSwan MCP Server

Real-time crypto risk intelligence via the Model Context Protocol. Two AI agents monitor what's happening 24/7 — before news breaks, as it breaks, and why it happened. Enables agents to have real-time risk intelligence and awareness, not yesterday's news.

Tools

Tool	What it answers
Flare	"Is something happening right now?" — Precursor detection from a 15-minute signal window
Core	"What's the overall risk environment?" — State synthesis from a 60-minute signal window

Connect

MCP

Point any MCP client at:

https://mcp.blackswan.wtf/mcp

No API key required.

REST API

curl -s https://mcp.blackswan.wtf/api/flare
curl -s https://mcp.blackswan.wtf/api/core

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "blackswan": {
      "type": "http",
      "url": "https://mcp.blackswan.wtf/mcp"
    }
  }
}

Then ask Claude: "What does Flare say about current crypto risk?"

Response Format

Flare

Field	Description
`agent`	Always `"flare"`
`data_age`	Human-readable age (e.g. "12 minutes ago")
`status`	`"clear"` or `"alert"`
`severity`	`"none"`, `"low"`, `"medium"`, `"high"`, or `"critical"`
`checked_at`	ISO 8601 timestamp
`assessment`	Natural language risk assessment
`signals`	Array of detected signals with `type`, `source`, and `detail`

Core

Field	Description
`agent`	Always `"core"`
`data_age`	Human-readable age (e.g. "1 hour ago")
`timestamp`	ISO 8601 timestamp
`environment`	`"stable"`, `"elevated"`, `"stressed"`, or `"crisis"`
`assessment`	Natural language risk assessment
`key_factors`	Array of main risk factors
`sources_used`	Data sources used in the assessment
`data_freshness`	How fresh the underlying data is

Interpreting Results

Flare Severity

Level	Meaning
`none`	No precursors detected, markets quiet
`low`	Minor signals, worth noting but not actionable
`medium`	Notable signals, warrants attention
`high`	Strong precursors, elevated risk of sudden moves
`critical`	Extreme signals, immediate risk of major event

Core Environment

Level	Meaning
`stable`	Normal conditions, low systemic risk
`elevated`	Above-normal risk, some stress indicators
`stressed`	Significant stress across multiple indicators
`crisis`	Severe market stress, active dislocation or contagion

Error Responses

HTTP Status	Meaning
`200`	Success
`502`	Agent output failed validation
`503`	No recent agent runs — system may be starting up
`500`	Unexpected server error

Non-200 responses return {"error": "..."} with a human-readable message.

License

MIT

This server cannot be installed

license - not found

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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