ENDPOINT 1
https://anyrow.ai/docs/mcp
MCP server metadata
- Name
- anyrow-docs
- Version
- 1.0.0
Anyrow's docs: guides and the REST API reference. Search with search_docs, then read pages with get_page or one endpoint with get_operation. Authenticate API calls with `Authorization: ApiKey <key>` against https://api.anyrow.ai.
Known tools 4
get_pageGet a docs page as Markdown, by its path or URL (`/docs/quickstart`, `/docs/api/rows/list`).
Inferred read-onlyget_operationGet one API operation's reference as Markdown: parameters, request body, response, errors and samples.
Inferred read-onlylist_pagesList every docs page (the site's llms.txt): guides and API resources with their Markdown URLs.
Inferred read-onlyCONNECT 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.anyrow-docs]
url = "https://anyrow.ai/docs/mcp"
enabled = true
Claude Code
.mcp.json
{
"mcpServers": {
"anyrow-docs": {
"type": "http",
"url": "https://anyrow.ai/docs/mcp"
}
}
}
Claude Desktop
Settings → Connectors → Add custom connector
Name: anyrow-docs
Remote MCP URL: https://anyrow.ai/docs/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": {
"anyrow-docs": {
"url": "https://anyrow.ai/docs/mcp"
}
}
}
Visual Studio Code
.vscode/mcp.json
Add to Visual Studio Code{
"servers": {
"anyrow-docs": {
"type": "http",
"url": "https://anyrow.ai/docs/mcp"
}
}
}
Generic MCP
Client-specific MCP configuration
{
"name": "anyrow-docs",
"transport": "streamable-http",
"url": "https://anyrow.ai/docs/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.
Evidence is source-attributed and does not guarantee that a third-party server is safe. Risk labels are conservative metadata heuristics.