Threads MCP server for your agent

claude mcp add --transport http threads-public-post-reply-and-profile-data "https://mcp.apify.com/?tools=fetch-actor-details,vexaloom-labs/threads-public-post-reply-and-profile-data"

1. Run the command in your project folder. Add --scope user to make it available in every project.
2. Start Claude Code, type /mcp, pick the server and sign in to Apify in the browser window.
3. No browser sign-in? Add --header "Authorization: Bearer YOUR_APIFY_TOKEN" to the command instead.

Sign-in: OAuth sign-in on first use, or a token header.

{
  "mcpServers": {
    "threads-public-post-reply-and-profile-data": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/actors-mcp-server",
        "--tools",
        "fetch-actor-details,vexaloom-labs/threads-public-post-reply-and-profile-data"
      ],
      "env": {
        "APIFY_TOKEN": "YOUR_APIFY_TOKEN"
      }
    }
  }
}

1. Easiest: open Customize, then Connectors, choose Add custom connector, paste the server URL (the ChatGPT tab shows it on its own) and sign in to Apify.
2. Local alternative (needs Node.js): merge this into claude_desktop_config.json (Settings, Developer, Edit config) and replace YOUR_APIFY_TOKEN.
3. Restart Claude Desktop and enable the server from the tools menu in a chat.

Sign-in: Connector with OAuth, or a token in the local config.

{
  "mcpServers": {
    "threads-public-post-reply-and-profile-data": {
      "url": "https://mcp.apify.com/?tools=fetch-actor-details,vexaloom-labs/threads-public-post-reply-and-profile-data",
      "headers": {
        "Authorization": "Bearer ${env:APIFY_TOKEN}"
      }
    }
  }
}

1. Save as ~/.cursor/mcp.json for every project, or .cursor/mcp.json for one project. Merge with servers you already have.
2. Set APIFY_TOKEN in the environment Cursor starts from, or replace ${env:APIFY_TOKEN} with your token.
3. Open Cursor Settings, then MCP, and check that the server lists its tools.

Sign-in: Token read from the APIFY_TOKEN environment variable.

{
  "inputs": [
    {
      "type": "promptString",
      "id": "apify-token",
      "description": "Apify API token",
      "password": true
    }
  ],
  "servers": {
    "threads-public-post-reply-and-profile-data": {
      "type": "http",
      "url": "https://mcp.apify.com/?tools=fetch-actor-details,vexaloom-labs/threads-public-post-reply-and-profile-data",
      "headers": {
        "Authorization": "Bearer ${input:apify-token}"
      }
    }
  }
}

1. Save as .vscode/mcp.json in your workspace.
2. Start the server from the file or the MCP servers view. VS Code asks for the token once and stores it securely.
3. Use the tools from Copilot Chat in agent mode.

Sign-in: Token prompted once and stored by VS Code.

https://mcp.apify.com/?tools=fetch-actor-details,vexaloom-labs/threads-public-post-reply-and-profile-data

1. In ChatGPT on the web, open Plugins, select + and choose Add custom MCP server.
2. Name it, paste the URL under Connection as the Server URL and choose OAuth for authentication.
3. Accept the risk warning (I understand and want to continue), select Create as a plugin, then install the plugin from your plugins, sign in to Apify and use it in a new chat.

Sign-in: OAuth sign-in with your Apify account.

[mcp_servers.threads-public-post-reply-and-profile-data]
url = "https://mcp.apify.com/?tools=fetch-actor-details,vexaloom-labs/threads-public-post-reply-and-profile-data"
bearer_token_env_var = "APIFY_TOKEN"

1. Add the block to ~/.codex/config.toml, or .codex/config.toml in a project.
2. Export APIFY_TOKEN in the shell that starts Codex. Or skip the token line and run codex mcp login to sign in.
3. Start codex and type /mcp to check that the tools are listed.

Sign-in: Token read from the APIFY_TOKEN environment variable.

gemini mcp add -t http -s user -H "Authorization: Bearer $APIFY_TOKEN" threads-public-post-reply-and-profile-data "https://mcp.apify.com/?tools=fetch-actor-details,vexaloom-labs/threads-public-post-reply-and-profile-data"

1. Export APIFY_TOKEN first: the shell expands it and Gemini CLI saves the header, token included, in plain text in ~/.gemini/settings.json (keep that file private). In PowerShell write $env:APIFY_TOKEN instead of $APIFY_TOKEN. Drop -s user to save it in the project's .gemini/settings.json.
2. Start gemini and type /mcp to check that the server is connected and lists its tools.
3. Saved by hand, a remote server uses the httpUrl key for streamable HTTP (url means the older SSE transport).

Sign-in: Token header saved by gemini mcp add.

https://mcp.apify.com/?tools=fetch-actor-details,vexaloom-labs/threads-public-post-reply-and-profile-data

1. Add an AI Agent node, then attach an MCP Client Tool node to its tool input.
2. Paste the URL as the endpoint and choose HTTP Streamable as the transport.
3. Choose Bearer auth and create a credential that holds your Apify token.

Sign-in: Bearer auth credential.