131 posts tagged with "MCP"

Ask your AI about pipeline data and get a real dashboard. Not text. Not a description. An actual interactive visualization you can click, filter, and explore.

MCP Apps (SEP-1865) is a recent extension to the Model Context Protocol that lets a server hand the host an interactive UI rather than only text or structured JSON. The host renders that UI inside its own surface — Claude Desktop, ChatGPT and Cursor have all been moving in this direction — and the UI can call back into the server through the existing MCP transport.

The reason it matters is that a lot of the things people want agents to do are not really text-shaped. A pipeline view, a comparison chart, a form with validation, a sortable table — these have always been awkward to express as a tool response. MCP Apps gives them a first-class home without throwing out the auth, sandboxing or audit story that MCP already had.

Deal flow management is messy. You're tracking companies, founders, opportunities, lists, notes, emails, meetings, calls. Jumping between screens trying to find who you talked to last week, what stage that deal is in, which companies need follow-up.

We built 35+ MCP tools for Affinity. Now you can manage your entire relationship intelligence workflow through conversation with Claude. Track companies, find people, manage opportunities, organize lists, review interactions. Just ask.

Google Analytics is overkill for most sites. You just want to know how many people visited, where they came from, and which pages they read. Instead you get 50 different reports, cookie consent banners everywhere, and a dashboard that takes five minutes to load.

So we built 17 MCP tools for Plausible Analytics. Now you can check your site stats through conversation with Claude. Simple, privacy-focused analytics without the complexity. No cookies, no invasive tracking, no endless dashboards.

Sales teams waste hours every day jumping between CRM screens. You know the drill. Checking lead statuses, scheduling follow-ups, updating pipelines, creating prospecting lists. It's exhausting and takes time away from actually selling.

So we built 32 MCP tools for NoCRM. Now you can manage your entire sales pipeline through conversation with Claude. Check lead statuses, schedule follow-ups, assign prospects, track activities. Just ask.

SEO monitoring is tedious. You're checking Search Console for rankings, analyzing traffic drops, hunting down indexing issues. Trying to figure out why some pages rank but don't get clicks. It's a lot of clicking around different reports and trying to connect the dots.

So we built 20 MCP tools for Google Search Console. Now you can manage your entire SEO workflow through conversation with Claude. Check rankings, diagnose indexing problems, find optimization opportunities, track trends—all by just asking. No more jumping between reports.

Running a warehouse is complicated. Orders come in, stock moves around, suppliers need managing, and you're constantly jumping between screens trying to keep everything straight.

That's why we built 101 MCP tools for Picqer. Now you can manage your entire warehouse through conversation with Claude. Check stock levels, process orders, create purchase orders, track shipments—all by just asking.

There's a better way to organize MCP servers that solves the context rot problem plaguing AI workflows everywhere.

OpenAI's Sora can generate videos from text prompts. Seriously impressive stuff. But the API's a bit clunky to work with directly.

That's why we built the Sora MCP bundle. 6 tools that let you create, remix, and manage AI-generated videos without touching code. Just tell the AI what you want and it handles the rest.

This video was generated using the Sora MCP bundle—we used our own tools to create it.

Anthropic's recent blog post about code execution with MCP got everyone excited about converting tool calls into code. But I think we're optimizing the wrong thing.

When you're building MCP tools, there's a moment where you realize something counterintuitive: the description field isn't just documentation—it's instruction. Every parameter description you write is a teaching moment where the AI learns not just what a parameter is, but when to use it, why it matters, and how it impacts the operation.

This shift in thinking—from documenting to teaching—changes how you design tools. Let me show you what that looks like in practice.

Here's a problem I kept running into: when you're building an MCP server, you face this weird tension between giving AI agents enough control and not drowning them in options. Build 20 different tools and you're burning context window on redundant functionality. Build 3 tools with no parameters and the AI can't do anything useful.

After shipping dozens of MCP integrations, I found something that actually works: six core tools that balance OpenAI's single-string requirements with rich, parameter-driven operations. It's not arbitrary—there's a reason this number keeps working.

When OpenAI integrated support for Anthropic's Model Context Protocol (MCP) into ChatGPT's deep research feature, they documented something elegant: a two-tool pattern that gives AI agents a consistent way to engage with any data source. If your MCP server implements search and fetch with their specific signatures, ChatGPT knows exactly how to explore your data without custom integration code.

Both tools accept only a single string parameter. That constraint isn't a limitation—it's what makes the pattern universal.

Weaviate is an open-source vector database that powers AI-native applications—RAG systems, semantic search, recommendation engines, and more. But how do you make a vector database accessible to AI agents? You can't just expose raw API endpoints and expect good results.

The answer: 6 focused tools organized around what developers actually do with vector databases. Not 20 tools covering every edge case. Not 3 tools that force you into awkward patterns. Just 6 tools that handle search, storage, browsing, and management—the core workflows every vector database application needs.

If you've ever installed an app on your computer—double-clicking a .dmg file on Mac or a .exe on Windows—you already understand .mcpb files.

Anthropic introduced the .mcpb extension (MCP Bundle) as the standard packaging format for distributing MCP servers. Think of it as the "app bundle" for AI tools: one file that contains everything needed to give your AI assistant new capabilities.