100% but this is not the same thing, nor is this going to replace the agent SDK (or visa versa). Agents will always need some form of communication protocol, if we look at the world and agentic frameworks its a sea of logos and without some forms of open standards this would be hard.
I'm currently at Comet and I have personally worked on MCP implementations AND have made some contributions to Agent SDK in the form of a native integration and improvement to test suite.
I think the key to what OpenAI is pushing towards is simplicity for developers through very easy to use components. I won't comment on the strategy or pricing etc, but on first glance as a developer the simple modular approach and lack of bloat in their SDK is refreshing.
Kudos to the team and people working on the edge to innovate and think differently in an already crowded and shifting landscape.
It is not only an Anthropic thing, and it works with any model that supports function calling, which DeepSeek did not when it first launched. That probably has changed since, but I haven't looked!
I don't like it. I don't like the OpenAI API all that much either but at least it's lightweight. I think MCP would fit better on mcp.anthropic.com to go along with their email address mcp-support@anthropic.com at the bottom of https://modelcontextprotocol.io/
I wish they'd done a smaller launch of it and gather feedback rather than announcing a supposed new standard which feels a lot like a wrapper.
This here is atrocious https://github.com/modelcontextprotocol/quickstart-resources... It includes this mcp PyPI package which pulls in a bunch of other PyPI dependencies. And for some reason they say "we recommend uv". How is that related to just setting up a tool for an AI to use?
That example code on DeepSeek doesn't actually include the logic to call a weather API? It just puts a fake answer back in, and you've got to handle the process manually.
The pyproject.toml in the Model Context Protocol example is just showing the new, "best" way to distribute and install Python projects and dependencies. If you haven't used uv before, it makes working with Python projects substantially better.
The Model Context Protocol server lets the model autonomously use the tool and incorporate its result. It's a much cleaner (imo obviously) separation of tool definition and execution.
I have been using Windsurf+Sonnet for a couple months, and recently adding Supabase MCP was a total game changer for velocity. I can't believe I waited so long to configure that.
Querying schema from prompt is great, but also being able to say "I cannot see the Create Project button on the projects list screen. Use MCP to see if user with email me@domain.com has the appropriate permissions" is just amazing.
MCP is a protocol, and Anthropic has provided SDKs for implementing that protocol. In practice, I find the MCP protocol to be pretty great, but it leaves basically everything except the model parts out. I.e. MCP really only addresses how "agentic" systems interact with one another, nothing else.
This SDK is trying to provide a bunch of code for implementing specific agent codebases. There are a bunch of open source ones already, so this is OpenAI throwing their hat in the ring.
IMO this OpenAI release is kind of ecosystem-hostile in that they are directly competing with their users, in the same way that the GPT apps were.
People are using the word "agentic" to mean this, I think. But yeah it's a dumb, overloaded primarily marketing word. I keep going back and forth on whether I should use the word "agentic"/"agent" at all
[edit] worth mentioning [langfuse](https://github.com/langfuse/langfuse), which is more like a platform that addresses the observability/evals/prompt management piece of the puzzle as opposed to a full-on "agent framework". In practice I have not yet run into a case where I needed something like what OpenAI just released, nor crewAI etc (despite it feeling like those cases may be coming)
