PulseMCP

The top MCP question from enterprise leaders right now: "How do I curate an approved list of servers for my org?" With 13,000+ MCP servers out there and new ones shipping daily, "just let people install things" isn't a strategy. So we teamed up with @tadasayy and @grumpygrowthguy from @pulsemcp to build a curated catalog of trusted MCP servers purpose-built for Speakeasy customers rolling out MCP at scale. Production-grade, vetted, enterprise-ready. 🤝

Software engineers worried about AI absorbing their craft would do well to embrace the change and do what the history of building software has always done: move up the abstraction layer. Agentic engineering is here. Teams are making it work. The portfolio of very public examples of this adoption grows by the day. I’ve been coaching some of the teams going through this shift in the software engineering trade - and I’ve found that the mindset shift often takes just two very specific “aha” moments.

Excalidraw in Claude. MCP Apps made by one of the main engineers behind MCP Apps: github.com/antonpk1/excal…

Congrats on the launch, @PlanetScale!

MCP Apps are officially here. 🎉 Officially released today by @AnthropicAI, @OpenAI, and MCP-UI (@idosal1 and myself). These apps will run *everywhere*. @claudeai, @code, @goose_oss, even @ChatGPTapp. Everywhere. *Check out the future of the web*: blog.modelcontextprotocol.io/posts/2026-01-…

@tadasayy @apify Thank you for publishing my little rant on my favorite AI newsletter @pulsemcp!

The MCP ecosystem faces a repeated "chicken or egg" problem with its long tail of rich features. Servers don't implement features until clients support them, & vice-versa. Sometimes the problem is even worse: servers can't adopt features lest they break (popular) laggard clients

@voxmatt , CEO of @getclockwise , takes us through the difference between data access and intelligent coordination when building MCP servers. Most MCP servers expose calendar data for LLMs to process, Clockwise's server does the scheduling optimization server-side and returns actionable proposals. One leverages the LLM's intelligence. The other leverages domain intelligence. Read it here: pulsemcp.com/posts/how-we-b…

There are several kinds of MCP servers out there: - Thin wrapper logic that could be an Agent Skill or a CLI tool - CRUD access that does little more than use MCP as a standardized auth mechanism - And the most compelling: buckets of hard-won domain intelligence That third category is where MCP really shines

We are donating MCP to the newly created Agentic AI Foundation. I am thrilled that we found a way to ensure that MCP will always remain neutral. Our commitment to MCP remains the same. We continue to be deeply involved and help steer the ship alongside other core maintainers from OpenAI, Google, AWS and Microsoft and the broader MCP community.

MCP Night is next week and we have a STACKED lineup 🔥 @dsp_ – co-creator of MCP @Anthropic @nicknotfun - MCP core @OpenAI Den Delimarksy - Eng @Microsoft @paul_irish – Eng @Google @ritakozlov – VP AI @Cloudflare @CraigCannon – Head of DevRel @Supabase @ReillyWood– Eng @datadoghq Plus some other surprise guests. It's going to be an incredible night. Make sure to RSVP now 🙇‍♂️

One of the biggest challenges with using agents today is figuring out how to provide the LLMs with a toolset that it understands how to use accurately, is not overly broad, and enables meaningful tasks to be completed. After testing out various approaches, we have found a use case driven way for packaging up tools to be the most effective approach. Our article with @pulsemcp goes into the details of what we've learned, why it is important to have tool groups based on use case or roles, and how it turns out that Model Context Protocol (MCP) already provides a natural way for us to implement this. We call this approach the Virtual MCP approach, where we use a gateway architecture to quickly group up tools by use case. Critically, tools in a virtual MCP come from multiple MCP servers. Using a virtual MCP setup makes it both easy for end users to configure, and also IT/admins to secure. As enterprises adopt more agents + MCPs, we think these approaches will become critical as we need control and telemetry into what the agents can and are doing. Thanks for @tadasayy @grumpygrowthguy for the collaboration. pulsemcp.com/posts/virtual-… @MintMCP_AI @MCP_Community

MCP gateways help solve aggregation but often contribute to tool overload. When you have 1000+ tools available, agents struggle and context windows explode. When a design team just needs a few tools across Figma, Linear, and Github MCP Servers, they are likely to end up unintentionally loading hundreds. Our friends @MintMCP_AI partnered with PulseMCP to share how they are tackling this problem with Virtual MCP Servers. Give it a read: pulsemcp.com/posts/virtual-…

"The work on MCP has completely revolutionized the agentic AI landscape" - Jensen Huang 🫡

MCP often gets criticized for its propensity to bloat context with “tool overload”. I think this criticism is unfounded. The reality is, you should never have more than a few MCP servers active in your context at a given time. And if a single MCP server has too many tools - that server needs a redesign. The rest can be solved with Agentic MCP Configuration.

Great coverage via @pulsemcp on Anthropic's new policy for their MCP directory TL;DR: if you're building consumer-facing agents and you don't ship remote OAuth for your MCP server, you're ngmi

We like how you think :)

Super excited to be included in the latest MCP Weekly Pulse by the @pulsemcp team! We're only in the 1st or 2nd inning of MCP and agentic systems, but I couldn't be more excited to be building open tooling for MCP developers. V1 of Shinzo is out now, DM me for access :)

How do you price something that's never been priced before? We're excited to share a guest post from @reactiverobot, founder of Ref, who tackled this exact challenge while building what may be the first standalone paid MCP server. Read it here: pulsemcp.com/posts/pricing-… His breakdown covers some unique challenges when pricing AI tools from wildly different usage patterns (solo devs vs. autonomous agents) to competing in ecosystems where most things are free.