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epanya.ai

Provides schema-validated web search and payment receipt lookup for AI agents.

x402 supported1 endpoint2 known toolsFirst detected July 25, 2026Last detected July 29, 2026

ENDPOINT 1

https://api.epanya.ai/mcp

No auth detected

MCP server metadata

Name
epanya
Version
0.1.0
Capabilities
tools.listChanged

Known tools 2

search

Schema-validated web search for AI agents.

Inferred read-only
lookup_receipt

Look up the public settlement receipt for an Epanya payment by its Base Sepolia transaction hash.

Potential side effects

CONNECT WITH APPROVAL

Client installation

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.epanya]
url = "https://api.epanya.ai/mcp"
enabled = true
Claude Code

.mcp.json

{
  "mcpServers": {
    "epanya": {
      "type": "http",
      "url": "https://api.epanya.ai/mcp"
    }
  }
}
Claude Desktop

Settings → Connectors → Add custom connector

Name: epanya
Remote MCP URL: https://api.epanya.ai/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "epanya": {
      "url": "https://api.epanya.ai/mcp"
    }
  }
}
Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code
{
  "servers": {
    "epanya": {
      "type": "http",
      "url": "https://api.epanya.ai/mcp"
    }
  }
}
Generic MCP

Client-specific MCP configuration

{
  "name": "epanya",
  "transport": "streamable-http",
  "url": "https://api.epanya.ai/mcp"
}
MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

TRUST AND VERIFICATION EVIDENCE

Trust Data Available

BuiltWith Trust API v2 evidence for epanya.ai was fetched 2026-07-26T05:42:40.110Z.

Trust status Neutral

epanya.ai is assessed as Neutral: No suspicious signals found, but no strong positive signal either

Indexed

Evidence is source-attributed and does not guarantee that a third-party server is safe. Risk labels are conservative metadata heuristics.