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apipie.ai

An MCP server that lists and retrieves APIpie documentation pages by category and summary.

1 endpoint2 known toolsFirst detected September 12, 2026Last detected September 12, 2026

ENDPOINT 1

https://apipie.ai/mcp

No auth detected

MCP server metadata

Name
APIpie documentation
Version
1.0.0
Capabilities
tools.listChanged

Known tools 2

get-page

Retrieves the full content and details of a specific APIpie documentation page.

Inferred read-only
list-pages

Lists all available APIpie documentation pages with their categories and high-level summaries.

Inferred read-only

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.apipie-documentation]
url = "https://apipie.ai/mcp"
enabled = true
Claude Code

.mcp.json

{
  "mcpServers": {
    "apipie-documentation": {
      "type": "http",
      "url": "https://apipie.ai/mcp"
    }
  }
}
Claude Desktop

Settings → Connectors → Add custom connector

Name: apipie-documentation
Remote MCP URL: https://apipie.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": {
    "apipie-documentation": {
      "url": "https://apipie.ai/mcp"
    }
  }
}
Visual Studio Code

.vscode/mcp.json

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

Client-specific MCP configuration

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

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

TRUST AND VERIFICATION EVIDENCE

Loading Trust v2 evidence…

Checking the associated registrable domain. The BuiltWith key remains server-side.

Indexed

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