AI & Machine Learning

solarly.ai

Lists and retrieves AI-readiness case studies for SaaS/AI brands, providing scores, findings, and related files.

1 endpoint2 known toolsFirst detected June 22, 2026Last detected June 22, 2026

ENDPOINT 1

https://solarly.ai/api/mcp

No auth detected

List Solarly AI-readiness case studies for well-known SaaS/AI brands.

Inferred read-only

Get the full Solarly AI-readiness case study for a slug, including score, sub-scores, findings, llm.txt and ai-agents.json.

Inferred read-only

CONNECT WITH APPROVAL

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

Codex

~/.codex/config.toml

[mcp_servers.solarly-ai]
url = "https://solarly.ai/api/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "solarly-ai": {
      "type": "http",
      "url": "https://solarly.ai/api/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: solarly-ai
Remote MCP URL: https://solarly.ai/api/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": {
    "solarly-ai": {
      "url": "https://solarly.ai/api/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "solarly-ai": {
      "type": "http",
      "url": "https://solarly.ai/api/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

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

MCP Inspector

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

TRUST AND VERIFICATION EVIDENCE

BuiltWith Trust API v2 evidence for solarly.ai was fetched 2026-08-04T04:18:28.779Z and is being refreshed.

Trust status Trusted

solarly.ai is assessed as Trusted: Domain runs a meaningful technology spend, consistent with a real business.

Indexed

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