正在加载视频...

视频加载失败

We compared the most popular Web Search MCP Servers with the same exact prompts on Fairies Exa Perplexity Firecrawl Bright Data Each server has their own unique strengths and limitations. See below the the results!

80,634 次观看 • 1 年前 •via X (Twitter)

6 条评论

nico 的头像
nico1 年前

@fairies_agent So hard to pick a favorite rn will be exciting to see where they take things

Prashant 的头像
Prashant1 年前

@fairies_agent quite insightful, thanks Robert for sharing this

BMs 的头像
BMs1 年前

@fairies_agent Let’s get it

Altera 的头像
Altera1 年前

@fairies_agent Show more comparisons

Frankie 的头像
Frankie1 年前

@nicochristie @fairies_agent the latency differences are very surprising

Sacha Uzan 的头像
Sacha Uzan1 年前

@fairies_agent Hey @GuangyuRobert, got 4/4 right with @Linkup_platform in with deep parameter - should check it out

相关视频

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

141,930 次观看 • 1 年前