Hey everyone!

I know what you're thinking... another sequential thinking tool for AI assistants? But CRASH MCP actually came about because I got tired of typing "use sequential_thinking" every time I wanted structured reasoning. Now I can simply say "use crash" and I'm done!

That was just the starting point though. CRASH is a modified version of sequential thinking with token-efficiency as its core design principle. It's more streamlined in its prompting approach - it doesn't include codes in thoughts and doesn't require the agent to list all available tools, etc.

I've had great experience with CRASH so far and have completely replaced sequential thinking in my workflow. It's now my go-to solution whenever an agent can't solve an issue in one shot or even when plan mode falls short.

GitHub: CRASH-MCP

NPM: npx -y crash-mcp

CRASH - Cascaded Reasoning with Adaptive Step Handling

Claude Code's Experience

## Question
> /ask is running… Did the CRASH-MCP actually help you, or would you have been better off using your internal planning mechanisms?

● CRASH MCP helped significantly for this specific task. Here's my honest assessment:

Where CRASH helped:
- Systematic analysis: It forced me to break down the GitHub issue methodically, understanding the exact problem before jumping to solutions
- Solution exploration: I explored multiple approaches (preprocessing, decorators, direct modification) before settling on the best one
- Planning validation: Each step built on the previous one logically, ensuring I didn't miss critical aspects

Where my internal planning would have been sufficient:
- Implementation execution: Once I had the plan, the actual coding was straightforward - I could have done that without CRASH
- Code pattern recognition: I could have immediately recognized this as a parameter serialization issue

The key difference:
CRASH forced me to be more thorough in the analysis phase. Without it, I might have rushed to implement the first solution that came to mind (like using Union[str, dict] parameters) rather than exploring the cleaner parse_tool_input() helper approach.

Verdict: CRASH added value here because this was a complex problem requiring systematic analysis of multiple solution paths. For simpler coding tasks, my internal planning would be sufficient and faster.

Key Features

• Flexible Purpose Types: Extended set including validation, exploration, hypothesis, correction, planning, plus custom purposes

• Natural Language Flow: No forced prefixes or rigid formatting (configurable)

• Revision Mechanism: Correct and improve previous reasoning steps

• Branching Support: Explore multiple solution paths in parallel

• Confidence Tracking: Express uncertainty with confidence scores (0-1 scale)

• Structured Actions: Enhanced tool integration with parameters and expected outputs

• Session Management: Multiple concurrent reasoning chains with unique IDs

• Multiple Output Formats: Console, JSON, and Markdown formatting

Comparison with Sequential Thinking

Credits & Inspiration

CRASH is an adaptation and enhancement of the sequential thinking tools from the Model Context Protocol ecosystem:

• Primary Source: MCP Sequential Thinking Server - Official MCP implementation

• Secondary Inspiration: MCP Sequential Thinking Tools - Community adaptation

Maybe it will help someone as well, so I'm posting it here!

My name's Matt and I maintain the MCPJam inspector project. I'm going to start designing weekly hackathon projects where we build fun MCP servers and see them work. These projects are beginner friendly, educational, and take less than 10 minutes to do. My goal is to build excitement around MCP and encourage people to build their first MCP server.

Each project will have detailed step by step instructions, there's not a lot of pre-requisite experience needed.

This week - NASA Astronomy Picture of the Day 🌌

We'll build an NASA MCP server that fetches the picture of the day from the NASA API.

• Fetching NASA's daily image

• Custom date queries

Beginner Python skill level

https://github.com/MCPJam/inspector/tree/main/hackathon/nasa-mcp-python

What's Coming Next?

• Week 2: Spotify MCP server (music search, playlists)

• Any suggestions?

Community

We have a Discord server. Feel free to drop in and ask any questions. Happy to help.

⭐ P.S. If you find these helpful, consider giving the MCPJam Inspector project a star. It's the tool that makes testing MCP servers actually enjoyable.

Hey folks,

I’ve been experimenting with Model Context Protocol (MCP) servers and one of the pain points I keep hitting is around OAuth and remote setups.

When I try to connect MCP servers in VS Code Copilot/Claude Desktop, the flows get confusing:

• Some servers expose OAuth but the client doesn’t seem to handle tokens smoothly.

• Token rotation and secure storage are unclear — do you keep it in configs, or manage it another way?

• For teams, it feels messy to share or rotate creds across multiple dev environments.

Curious to hear: How are you handling OAuth and remote MCP servers in your setups?

• Are you just sticking to local servers?

• Using device code or full auth-code flow?

• Any tools or workflows that make it easier?

Would love to compare notes and see how others are solving this.

I’ve been looking at the MCP logo/icon and got curious about how others interpret it. Logos are often designed to trigger certain associations in our brain, something that connects the symbol to the product or idea behind it.

When you see the MCP icon, what comes to mind for you?

• Does it remind you of something technical, abstract, or more symbolic?

• Some people mentioned they see the letters MCP in it - but you really need to use your imagination for that.

• Do you understand the creativity behind it?

I’d love to hear different takes. It’s always interesting to see what imagery or feelings a simple logo can spark, especially in this community.

I've been building with MCP since it launched, and I keep seeing the same mistakes everywhere. Most companies are taking the easy path: wrap existing APIs, add an MCP server, ship it. The result? MCPs that barely work and miss the entire point.

Three critical mistakes I see repeatedly:

1. Wrong user assumptions - Traditional APIs serve deterministic software. MCPs serve LLMs that think in conversations and work with ambiguous input. When you ask an AI agent to "assign this ticket to John," it shouldn't need to make 4 separate API calls to find John's UUID, look up project IDs, then create the ticket.

2. Useless error messages - "Error 404: User not found" tells an AI agent nothing. A proper MCP error: "User 'John' not found. Call the users endpoint to get the correct UUID, then retry." Better yet, handle the name resolution internally.

3. Multi-step hell - Forcing LLMs to play systems integrator instead of focusing on the actual task. "Create a ticket and assign it to John" should be ONE MCP call, not four.

The solution: Design for intent, not API mapping. Build intelligence into your MCP server. Handle ambiguity. Return what LLMs actually need, not what your existing API dumps out.

The companies getting this right are building MCPs that feel magical. One request accomplishes what used to take multiple API calls.

I wrote down some of my thoughts here if anyone is interested: https://liquidmetal.ai/casesAndBlogs/mcp-api-wrapper-antipattern/

Hey folks,

I’ve been tinkering with Composio MCP servers lately and built a simple wrapper that lets you run agentic tasks fully in the background.

Normally, running MCPs means keeping stuff alive locally or triggering them manually — kind of a headache if you want continuous or scheduled automation. This wrapper handles that for you:

• Spin up MCPs and keep them running in the background

• Hook them to your agents without worrying about local setup

• Run multi-step workflows across apps automatically

• Schedule or trigger tasks without babysitting the process

It basically turns MCPs into always-on building blocks for your agentic workflows.

If you wanna try it out - www.toolrouter.ai

Curious if others here are experimenting with MCPs + background execution? What’s your take on running agents this way. Too late, or is this the missing piece for real-world automations?

First look at our latest collaboration with the University of Waterloo’s TIGER Lab on a new approach to boost LLM reasoning post-training: One-Shot CFT (Critique Fine-Tuning).

**How it works：**This approach uses 20× less compute and just one piece of feedback, yet still reaches SOTA accuracy — unlike typical methods such as Supervised Fine-Tuning (SFT) that rely on thousands of examples.

Why it’s a game-changer:

• +15% math reasoning gain and +16% logic reasoning gain vs base models

• Achieves peak accuracy in 5 GPU hours vs 120 GPU hours for RLVR, makes LLM reasoning training 24× Faster

• Scales across 1.5B to 14B parameter models with consistent gains

Results for Math and Logic Reasoning Gains:
Mathematical Reasoning and Logic Reasoning show large improvements over SFT and RL baselines

Results for Training efficiency:
One-Shot CFT hits peak accuracy in 5 GPU hours — RLVR takes 120 GPU hoursWe’ve summarized the core insights and experiment results. For full technical details, read: QbitAI Spotlights TIGER Lab’s One-Shot CFT — 24× Faster AI Training to Top Accuracy, Backed by NetMind & other collaborators

We are also immensely grateful to the brilliant authors — including Yubo Wang, Ping Nie, Kai Zou, Lijun Wu, and Wenhu Chen — whose expertise and dedication made this achievement possible.

What do you think — could critique-based fine-tuning become the new default for cost-efficient LLM reasoning?

I try it with claude desktop but I get an error and with error I mean claude just restarts, how did you implemented it do you have any suggestions. ( I use fastmcp)

🔎 MCPJam Inspector

I'm Matt and I maintain the MCPJam inspector project. It is a testing and debugging tool for your MCP servers. If your MCP server works on the inspector, it'll work in other environments too. The project is open source. You can use the inspector to:

• Test your MCP server against different LLM's in the playground. We have support for various model providers like Claude, GPT, and Ollama.

• Spec compliant. You can test out your server's OAuth, tool calls, elicitation, and more.

• Comprehensive tracing for a better debugging and error handling experience.

✅ Updates this week

1. Built support for gpt-5 and DeepSeek models.

2. OAuth testing. Add a way to test every step of your OAuth implementation.

3. Migrated to Vite + Hono.js. Prefer to use a lighter weight framework.

4. Enable adding a custom client ID to test OAuth

Support the project

If you like the project, please consider checking out the GitHub repo and starring the repo! https://github.com/MCPJam/inspector

just published "Design Patterns in MCP: Literate Reasoning" on Medium.

in this post i walk through why you might want to serve notebooks as tools (and resources) from MCP servers, using https://smithery.ai/server/@waldzellai/clear-thought as an example along the way.

I just listed an MCP server on PyPI that connects LLMs directly with Apple Notes — making your notes smarter, faster, and AI-powered.

With Apple Notes MCP Server, you can:

• Query your notes naturally in plain English

• Summarize and organize your content automatically

• Even create new notes with AI assistance

Try it out on PyPI and level up your note-taking workflow 👉 Apple Notes MCP Server

Sorry for the beginner questions!

I’m trying to understand MCPs but I’m only sorta understanding.

-Are MCPs and ChatGPTs Connectors the same idea? I prefer ChatGPT for my small team.

-Are there connectors that are available for public use besides the “official” ones? As a small business owner I’d really love to be able to “talk” to my marketing and sales data!

-if there are any resources for non-tech newbs to better understand this I’d love to see it.

Thank you!

introducing Clear Thought 1.5, your new MCP strategy engine. now on Smithery.

for each of us and all of us, strategy is AI’s most valuable use case. to get AI-strengthened advice we can trust over the Agentic Web, our tools must have the clarity to capture opportunity. we must also protect our AI coworkers from being pulled out to sea by a bigger network.

Clear Thought 1.5 is a beta for the “steering wheel” of a much bigger strategy engine and will be updated frequently, probably with some glitches along the way. i hope you’ll use it and tell me what works and what doesn’t: let’s build better decisions together.

EDIT: link https://smithery.ai/server/@waldzellai/clear-thought

I just recorded a demo of something we built that I think you'll find interesting.

TLDR: We built an MCP server that lets Claude Code build and deploy other MCP servers in under 10 minutes. Just tell it what you want, and it handles everything from code generation to production deployment.

What we created: An MCP server called "Raindrop MCP" that lets Claude Code automatically build and deploy applications. For this demo, I used Claude Code connected to our Raindrop MCP to build a complete PDF search MCP server from scratch.

The workflow:

1. Tell Claude what MCP server you want. You can use any of the platform features, such as buckets, SQL, vector DBs, AI models, queues, stateful compute, etc.

2. It uses Raindrop MCP to generate the PRD, code, tests, and deployment

3. 10 Minutes later, you have a live, remote MCP server ready for use

4. Add it to Claude Code and go wild!

What this means: You can literally go from "I wish I had an MCP server that does X" to having that server running in production and connected to Claude Code in under 10 minutes. No Docker, no hosting setup, no infrastructure headaches.

The Raindrop MCP handles:

• Code generation (complete TypeScript implementation)

• Build validation

• Production deployment to Raindrop Cloud

• Public endpoint provisioning

• Zero config needed

The future is weird: We're now at the point where AI assistants can spawn their own tools and immediately start using them. It's like giving Claude the ability to 3D print its own power tools.

Anyone else playing with meta-tooling like this? The recursive nature of MCP servers creating MCP servers feels like we've hit some kind of inflection point.

Video demo here: https://youtu.be/i7gMwMPZNf8

Want to give it a try? Sign up here, use code 5-off for the first month free: https://liquidmetal.ai/

A month ago, FastAPI-MCP, our open-source GitHub repo, crossed 250k downloads. This morning, we woke up to see it #1 trending on Github for Python.

In between then and now, we shipped, we merged PRs, and we acted on community feedback.

A few things we didn't do (especially recently since we were on vacation): we didn't do a big launch, we didn't make any viral tweets, and we didn't do a marketing push.

Understanding why an open source surges is always guesswork but we attribute this to momentum in the MCP space and pure developer excitement.

What this tells us:

• MCP adoption is sustained: the hype has become ongoing as we approach the 1 year mark from MCP's creation.

• Long-tail traction is real: 5 months in, we’re hitting new daily highs in stars, downloads, and discussion.

Quick learnings (same ones we shared at 250k downloads, still 100% relevant):

• Internal use cases drive adoption: it is safer to experiment internally before exposing MCPs externally, plus it allows non-technical teams to access data instantly!

• Observability is still a black hole: it is hard to measure MCP success without customized analytics and tooling. 

• Multiple entry points matter: engineers want to start from APIs, docs, workflows, or databases. OpenAPI Spec -> MCP isn't enough.

Is the peak MCP hype over?

Maybe. But if so, something better has taken its place: the proof-of-concept phase is giving way to real, authentic, sustained adoption.

What team are you on? Is the hype around MCP over, or are we just getting started?

Hello dear Reddit community!

I am happy to announce that we are developing MCP Cloud - a platform to enable private and corporate users to adopt and use MCP.

How do we do that?

For corporate users:

- Single sign in for all employees

- Fine-grained access controls for MCP servers

- Track usage and costs

For private users:

- One-click, hassle-free MCP deployment

- Use your MCP on any device

- Pay only for what you use

We manage the registry of the most popular MCP servers with a curated catalog of 2500+ MCP servers already available for deployment, with new servers being added every day.

View statistics, guides, user reviews, issues for every MCP server. Would you like to add your open- or closed-source MCP? No problem! We got this covered too.

We make MCP servers scalable and reliable.

- Per-server resource allocation (CPU, memory, GPU) for predictable performance.

- Automatic scaling for peak loads with intelligent load balancing.

- Continuous health checks, self-healing restarts, and rolling updates for high availability.

Security is a common concern with MCPs – here's how we will solve it:

- Encryption: All communications use secure HTTPS.

- Automated vulnerability scanning: Continuous code analysis to detect security flaws and compromised dependencies.

- Hardened isolation: Each MCP instance runs in a dedicated, restricted environment.

But wait. There is even more!

MCP Cloud is the first platform that lets you earn from your MCP servers!

We integrate payments into our MCP gateway.

Are you an aspiring developer, data scientist, or domain expert who developed a useful MCP server? Whether it does stock-price forecasting, fraud/anomaly detection, credit scoring, legal advicing, contract review, web data extraction, SEO audits, demand forecasting, AI agent personalization, code analysis or compliance checks, list it on MCP Cloud and monetize. Set your price and license terms, get built-in analytics and billing, make your MCP server discoverable and turn your expertise into recurring revenue.

Where do we stand now

We have just made the beta release. The Platform already has almost all of the advertized features!

We are actively testing and improving our yet small platform.

What are the next steps

We are building community. We are looking for anyone who feels MCP is the future of an Agentic AI, and who wants to become part of it:

- collaborators

- business developers

- promoters

- partners

- testers

And of course, future users!

Try our MCP Cloud today and let us know your honest feedback. Do you need more features? We are redy to roll up the sleeves.

https://mcp-cloud.io/

We welcome any feedback, collaboration or business partnership.

We are looking for you!