MCP server

Oakferry Agents Official

com.oakferry/agents

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

Search and compare Oakferry's apps with dated, sourced facts: platforms, prices, ratings, limits.

Website

At a glance

Type

MCP server

Publisher

oakferry.com (verified by the MCP Registry)

Status

Working at last check

Tools

4

Token cost

About 587 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http agents https://agents.oakferry.com/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.agents]
url = "https://agents.oakferry.com/mcp"

Gemini CLI

{"mcpServers":{"agents":{"httpUrl":"https://agents.oakferry.com/mcp"}}}

Cursor

{"mcpServers":{"agents":{"url":"https://agents.oakferry.com/mcp"}}}

VS Code

{"servers":{"agents":{"type":"http","url":"https://agents.oakferry.com/mcp"}}}

Antigravity

agy mcp add agents https://agents.oakferry.com/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"agents","server_url":"https://agents.oakferry.com/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"agents","server_url":"https://agents.oakferry.com/mcp"}

Tools

• compare_products ~119 tokens
  Comparison tables of products and their alternatives on shared attributes, each row sourced. Pass an id to get one table, or a product id to get every table it appears in, or nothing to list them.
• get_product ~104 tokens
  Full facts for one product: where to get it, every price, ratings with dates, features, attributes, when not to recommend it, third-party coverage and sources.
• list_attributes ~78 tokens
  Filterable attribute keys, the values products use for each, and how many products set it. Use these keys in search_products attributes.
• search_products ~286 tokens
  Find products matching a person's constraints. Put hard requirements in the filters (platform, max_price, free, no_subscription, attributes) and the rest in query. Each result says why it matched. Call list_attributes to see filterable keys.

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Badge for your README

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