MCP vs CLI for LLMs is the wrong discussion. People are debating at the wrong layer. Both work incredibly well as long as they are run through a repl like environment that can persist state. The funny thing about CLI is that CLI tools are usually accessed through BASH which happens to be a repl with persistent state (the file system), therefore cli works much better than mcp. But this isn't even close to being an intrinsic property of MCP. Just means that we need better harnesses. Right now the best repl for this are: - bash + fs - jupyter kernels - codemode type repls, usually quickjs LLMs understand the concept of forward evolving a system to solve a need very well. This comes from the agentic RL. They understand how to change the state of a codebase or acquire data from databases and then operate on it similarly to humans. But without an execution environment, they actually cannot do this properly. My bet? Sometime soon someone will (or has already?) create a embeddable, sqlite style mini execution environment that parses bash, typescript, or tool calls into a common IL execution plan that's easy to security check before executing. And design this specifically for durable execution environments. Then we will just connect cli, mcp, webmcp, whatever to that and it accepts any of the input modalities as they can all be represented as each other.

MCP meets agent skills MCP already gave agents a standard way to connect to tools, resources, and external systems. Now it also defines a standard way to discover and load Agent Skills directly from MCP servers. The flow is simple: → connect to MCP server → discover available skills → inspect skill metadata → load the relevant 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱 only when needed Under the hood, Skills are served through MCP’s existing Resources primitive. That means 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱, references, scripts, examples, and other supporting files are exposed as resources that the client can read on demand. This is especially useful for context window management. Instead of loading every workflow instruction upfront, the agent can first discover what skills are available and pull in only the one required for the current task. A useful mental model is: tools = what the agent can do resources = what the agent can access skills = how the agent should perform a reusable workflow Previously, that workflow knowledge often lived separately in docs, repos, prompt files, or custom integrations. Now the MCP server can expose the capability and the playbook for using it together. So you get: → standardized skill discovery → on-demand context loading → cleaner distribution and versioning → reusable workflows that travel with the server MCP was already the connection layer. Skills now add a standardized way to ship reusable agent know-how on top of it. The illustration below visually summarizes everything that we discussed so far. Read more: Cheers! :)

MCP was wrong from the start but they finally fixed it. The 2026-07-28 spec removes initialize and Mcp-Session-Id entirely. There is no connection to hold open anymore. No more sessions. We explain the changes

MCP: new enough to be exciting, old enough to create a sprawl problem. Tell me if this sounds familiar. One team spins up an MCP server. Then five more. All built differently of course! Nobody knows what's out there or who owns what. Yikes. Agents got flooded with tools they don't need and you accidentally spent a months worth of tokens in a day (and you can't even blame Fable). Shadow infra at it's finest! But now it's got AI speed, so it's like supercharged chaos. That's why we built a central gateway for all AI context. One place to enforce authentication and security for all your data and tools. One point of observability to see which tools are being used, by which agents, and at what cost.