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Connecting an agent to more tools sounds like pure progress. Each new MCP server adds capability, and each one also adds text that the model must read before it can do anything useful. Tool names, descriptions, and input schemas all take up space in the context window. Large tool results take up more. Left unmanaged, this context bloat raises cost, slows responses, and can make the agent worse at choosing the right tool. This blog explains where the tokens go, how to measure

Teams building AI agents keep running into two ideas that sound similar and do different jobs: skills and MCP. Both extend what an agent can do. Mixing them up leads to bloated prompts, missing connections, and systems that are harder to maintain than they need to be. In short, MCP gives an agent access to tools and data. Skills give an agent know-how: instructions, procedures, and resources for doing a kind of task well. One answers the question of what the agent can reach.

AI agents are learning to use websites the way people do. They read the page, find a button, click it, and hope the layout has not changed since the last visit. It works, and it is fragile and slow. WebMCP proposes a different approach. Instead of guessing how a page works, an agent asks the page what it can do. The website publishes a list of tools with clear names, descriptions, and inputs, and the agent calls them directly. This blog explains what WebMCP is, how it works,

Most teams build their first MCP server as a single process on one machine. It works well in development, and the design questions only appear later, when traffic grows, a second instance is added, or a deployment restarts the server in the middle of someone's work. At that point, one question decides a lot: does the server remember anything between requests? A stateless MCP server does not. Every request carries what the server needs, and any instance can answer it. This blo

Two protocol names keep appearing in discussions of AI agents: MCP and A2A. They are often mentioned together, and they are sometimes treated as rivals. In practice, they solve different problems. MCP, the Model Context Protocol, connects an agent to tools and data. A2A, the Agent2Agent protocol, connects one agent to another. A single agent system can use both, and many serious multi-agent designs will. This blog explains what each protocol does, how they differ, how they fi

Every useful AI agent needs a way to act: search a database, send a message, create a ticket, read a file. Two approaches dominate how language models are connected to such tools. Direct function calling defines the tools inside your application. The Model Context Protocol, or MCP, defines them in separate servers that any compatible application can use. Both work, and they suit different situations. This blog explains how each one works, compares them on the points that matt

The usual way an AI agent uses tools is simple. The model picks a tool, the system runs it, the result goes back to the model, and the cycle repeats. It works well for a handful of tools and short tasks. It strains as the number of tools grows. Every tool definition sits in the model's context, and every intermediate result passes through the model on its way to the next step. Code Mode MCP takes a different route. Instead of calling tools one at a time, the agent writes a sm

The Model Context Protocol makes it easy to connect AI assistants to company systems, and that ease is exactly why security needs attention early. An MCP server can expose databases, files, tickets, and actions to a model that reads untrusted text and sometimes follows it. If the server is open, over-permissioned, or silent, one clever message can turn a helpful assistant into a data leak. This blog explains how to secure MCP servers around three controls: authentication, per

Every enterprise AI project hits the same wall: the model is capable, but it cannot reach the systems where the work happens. Connecting an assistant to a ticketing tool, a document store, a database, and a CRM has usually meant a custom integration for each pairing, rebuilt whenever anything changes. The Model Context Protocol (MCP) is an open standard that replaces this with one common way for AI applications to discover and use tools and data, which is why it is now a prio

The most comprehensive curated list of Model Context Protocol (MCP) project ideas — from beginner-friendly to advanced. Whether you're a developer looking to break into AI-powered tooling, a startup founder exploring automation, or a student building your portfolio, this list has something for every skill level. Browse by category, filter by difficulty, and start shipping.