MCP tools extend AI superpowers and give to us huge opportunities to enhance our agents. But here’s the hidden cost: every server adds context, even when idle. Every unused MCP is burn our precious tokens. That's why better to disable MCP, which we don't use often or use tools, as `hypertool-mcp` to load only the essentials. After weekly clean up unused MCP, you’ll see the token savings immediately.
How to save tokens by disabling unused MCP tools
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🚀 Just published a new Medium article Spent few days deep-diving into building MCP Server tools and testing with Amazon Q CLI agent, and one thing stood out: schema validation with Zod can transform how robust your tool is. In this piece, I share key learnings from implementing schema validation while building Model Context Protocol (MCP) tools—how it helped catch errors early, guide users, and make the tools more robust. If you’re building MCP tools and want smoother user input handling, this might be useful! #AI #MCPServer #AmazonQCLI
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Have you tried Kontent.ai’s MCP server yet? 🤔 It makes it possible for you to connect your Kontent.ai project to AI agents and services, enabling all new types of workflows. Michael Berry gives a quick introduction in the video below.👇 Want to learn more? Check out the links in the comments!
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Whenever you deploy the LLM Application make sure you trace each and every request, response. It tell you things like: - input, output tokens - Cost for LLM Models (most important) - Tracing for Tool Calling Also it is recommended to use the Pool of the Prompts for different Scenario for Answer Generation or task execution. All these things are possible with Langfuse #MLOPS #GenerativeAI #ArtificialIntelligence
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How do you go from a blank project to your first fully tested AI agent? In our latest blog, we share how we adapted Test-Driven Development (TDD) into the LLM era, using evals to define desired behaviors before writing code. By supercharging Claude Code with Model Context Protocol (MCP) servers, we grounded the agent in the right context (docs, repos, wikis). ✔️ Faster setup of evals with Eval Protocol ✔️ Richer, more reliable test suites generated automatically ✔️ A safer workflow for iterating on prompts, models, and features without regressions This shift turns AI into a true development partner- expanding tests, validating behaviors, and helping engineers scale agent capabilities with confidence. 👉 Read the full blog here: https://lnkd.in/gQX5aN-6
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Why Model Context Protocol (MCP) is the “HTTP” Moment for AI Agents 🚀 Just like HTTP standardized the web, MCP is standardizing how LLMs interact with tools, memory, and external data. ✅ #Problem #Today: Every framework (LangChain, LlamaIndex, etc.) defines its own “agent tools” → siloed, hard to scale. ✅ #MCP #Solution: One universal standard → “USB-C for AI tools.” 🔑 Key Benefits: – Standardization across industry – Simplified tool integration (no reinvention) – Interoperability & ecosystem growth – Foundation for advanced agentic applications MCP is the missing piece for making AI agents enterprise-ready. 🌍 👉 Would you adopt MCP in your next AI project? #ModelContextProtocol #MCP #AIagents #AgenticAI #GenerativeAI #AItools #EnterpriseAI #AIInnovation
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Scientific Software Engineer / PhD in biomedical information science and knowledge engineering
To people wrapping the entire world with MCP: if a tool already has a good, self-documented command-line interface, you probably DON'T need to turn it into an MCP server. If the CLI of a tool makes it difficult for an agent to use it, please do not write an MCP interface to make it smoother for AI agents. Write a *better* command-line interface to make it smoother for AI agents *and* humans *and* scripts. If something can just run locally and serialize its state to disk, it should be a regular CLI tool, simple as that. Thus, example of a questionnable MCP use-case: https://gitmcp.io/
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When initially announced, switching to GPT-5 seemed like a no-brainer. Cheaper (per token), maybe faster and more intelligent, but... When using a reasoning mode (where the performance gains are mostly) it can be slower and use >3x as many tokens for each request. So, for a marginal performance improvement your users have to wait longer (and maybe bounce) and our margins fall, sometimes significantly (so we may have to charge more). Suddenly, not so easy a decision. That's why you probably haven't seen a whole load of businesses that rely on speed and affordability switching right away. It will come, undoubtedly, and we are looking at ways to get the best bang for the buck from the model, but definitely wasn't the no-brainer we initially hoped for!
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MLOps is the key to unlocking scalable, efficient machine learning solutions. But many companies struggle with deployment, monitoring, and governance. Mission helps organizations navigate the complexities of ML—from model training to real-world implementation. See how 🔗 https://ow.ly/4tOB50V8BnG
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AI Agents Developer & Full-Stack Engineer | Enhancing Digital Solutions with Design Thinking
🚀 For AI Agent Developers! 🚀 If you’re building with MCP (Model Context Protocol), you know how powerful it is for connecting agents with external tools and data. But here’s the challenge: finding reliable, ready-to-use MCP servers can be time-consuming. 💡 That’s why I’m sharing this incredible resource — the biggest curated list of MCP servers available today: 👉 https://lnkd.in/dSzMSZ8P This repo is a goldmine for: ✅ Exploring useful MCP servers ✅ Speeding up your agent development ✅ Discovering integrations you didn’t know existed If you’re serious about AI agents + MCP, this is a bookmark-worthy resource. #AI #MCP #AgentDevelopers #OpenAI #DevTools #Innovation
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