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📬 I built an MCP server that lets LLMs search my email from the terminal

The server connects Claude to email search via the mu CLI tool. Now I just ask it things like: “Find emails with PDF attachments from last April” ⚡

🛠 No custom frontend. No heavy framework. Just a CLI tool made smarter.

💡 I learned that MCP servers are basically API translators — they take complex developer SDKs and flatten them into simple function calls that LLMs can actually use.

🎯 The bigger picture: This pattern can breathe new life into existing CLI tools and services. Complex APIs → Simple, declarative functions → Natural language queries.

This isn’t a product — just an experiment in stitching new capabilities into existing workflows. Code here: https://lnkd.in/eT2fJBSv

mu email indexer and searcher: https://github.com/djcb/mu

#MCP #LLM #EmailSearch #OpenSource #AI

What existing tools would you want to make LLM-friendly? 🤔 To view or add a comment, sign in Whenever I am building complex 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐒𝐲𝐬𝐭𝐞𝐦𝐬, I mostly end up adding a lot of 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 to the system. And personally, these two techniques have always helped me a lot with reducing latency:

• 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: If your agent has several steps that don’t depend on each other, you can run them at the same time instead of one after another — this makes things much faster. But in a real production system, you have to be careful to avoid data conflicts (known as data races) when those parallel steps access shared information.

In that case, if you are running five processes inside the logic of your agent, and all of them are taking 3 seconds: Without parallelization: 3x5 = 𝟏𝟓𝐬 (𝐁𝐚𝐝 𝐔𝐗) With Parallelization: 3x1 = 𝟑𝐬 (𝐆𝐨𝐨𝐝 𝐔𝐗)

• 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠: Sometimes, you can’t make your agent actually faster without sacrificing quality. In that case, focus on improving how fast it feels to the user — what’s called perceived latency. You can do this by showing helpful updates while the agent works — like a progress bar, a list of key actions it’s taking, or even streaming the AI’s response live, word by word, as it’s being generated.

Because streaming keeps the user engaged. If you are a web developer, you know the importance of a loader when a process is happening or waiting for an API response. If you have used Cursor or some coding agent, you would have experienced that it shows you:

• The todos it is organizing
• The code it is reading
• The Edits it is making
• Different files it is working on
• Different commands it is executing Guess what if it doesn’t show us anything and only comes after 5 minutes. I may end up closing it mid-way for sure 😅 That’s the importance of Streaming

𝑊ℎ𝑎𝑡 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡 𝑡𝑒𝑐ℎ𝑛𝑖𝑞𝑢𝑒𝑠 𝑑𝑜 𝑦𝑜𝑢 𝑝𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑙𝑦 𝑢𝑠𝑒 𝑡𝑜 ℎ𝑒𝑙𝑝 𝑤𝑖𝑡ℎ 𝑡ℎ𝑒 𝑙𝑎𝑡𝑒𝑛𝑐𝑦 𝑖𝑛 𝑐𝑜𝑚𝑝𝑙𝑒𝑥 𝐴𝑔𝑒𝑛𝑡𝑖𝑐 𝑠𝑦𝑠𝑡𝑒𝑚𝑠?

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You've built your FastAPI application. Tests pass. It works locally. Now you're reading deployment guides and drowning in advice about connection pool tuning, PostgreSQL JIT compilation, and async event loop optimization.

Here’s the problem: you’re optimizing blind. You don’t have production traffic to measure. You don’t know where your bottlenecks are.

Meanwhile, the stuff that will actually break on day one gets buried in the noise. I’ve seen developers spend days tuning connection pools for traffic they don’t have yet, while missing the fact that their authentication breaks in production or their database credentials aren’t set correctly.

The truth is, before your first deployment, only three things actually matter:

• Don’t get hacked (security configurations that prevent immediate exploitation)
• Don’t break immediately (configuration that prevents instant failures)
• Know what’s happening (minimal observability so you can actually debug issues)

Everything else - performance tuning, advanced monitoring, sophisticated caching - can wait until you have real data.

I just published a practical checklist covering exactly what matters in the hour before you go live. No overwhelming theory. Just the non-negotiables explained with real examples of what happens when you skip them.

Link in the comments!

#python #fastapi #webdev #deployment

Want to skip deployment configuration entirely? Check out FastroAI at https://fastro.ai - a production-ready FastAPI template with security, monitoring, and deployment already configured correctly. To view or add a comment, sign in Tried my hands on web scraping and AI-powered document processing recently.

I built a pipeline that crawls configured websites, filters PDFs by exam type and year, and downloads them in a structured way. Both the exam name and years are configurable through the config file.

Instead of using traditional parsing methods, I integrated Claude (Sonnet 4) to directly read PDFs, extract questions and options, and tag them with subject, topic, difficulty level, and many more attributes — all in one step. The processed data exports to Google Sheets for easy analysis and organization.

The project includes three CLI commands for crawling, tagging with Claude, and exporting to Sheets, keeping the workflow modular and composable.

Here’s a demo dataset from one of the runs showcasing the structured output. This setup uses exam papers from two years: https://surl.li/rhsnmg

Tech stack: Node.js, TypeScript, Claude API, Google Sheets API

GitHub Repo: https://lnkd.in/g4kdAvFE

#AI #WebScraping #Automation To view or add a comment, sign in

              446 followers
          Actions, Not Just Chat   React Component GPT:

We need a GPT that understands our React components, knows our CSS variables, and can spit out code that’s ready to use. This isn’t about general knowledge; it’s about our knowledge. The standard GPT knowledge upload is fine for broad docs, but for precise component generation, we need control. That’s where Actions come in. Our design system lives in zeroheight. Our CSS variables are in a .css file. Our React components are in .jsx files. These are all discrete sources of truth. A generic LLM has no idea how they connect. If someone asks for a “primary button,” it might give generic HTML, not our Button component with –color-brand-primary. Unacceptable. We build an API. This API becomes our “knowledge retrieval service.” The GPT uses Actions to call this API when it needs specific, localized data. Extract Data (The ETL of our Design System): zeroheight Content: Use the zeroheight API to pull down all component documentation. Store it, parse it, clean it. We’re i https://lnkd.in/gufWti_X To view or add a comment, sign in

              299 followers
          Actions, Not Just Chat 

React Component GPT:

We need a GPT that understands our React components, knows our CSS variables, and can spit out code that’s ready to use. This isn’t about general knowledge; it’s about our knowledge. The standard GPT knowledge upload is fine for broad docs, but for precise component generation, we need control. That’s where Actions come in. Our design system lives in zeroheight. Our CSS variables are in a .css file. Our React components are in .jsx files. These are all discrete sources of truth. A generic LLM has no idea how they connect. If someone asks for a “primary button,” it might give generic HTML, not our Button component with –color-brand-primary. Unacceptable. We build an API. This API becomes our “knowledge retrieval service.” The GPT uses Actions to call this API when it needs specific, localized data. Extract Data (The ETL of our Design System): zeroheight Content: Use the zeroheight API to pull down all component documentation. Store it, parse it, clean it. We’re i

https://lnkd.in/gufWti_X To view or add a comment, sign in

              446 followers
          API-Mocker Hits 5.38K Downloads: The Open Source API Development Platform That's Changing How Developers Mock APIs   The Problem Every Developer Faces

Building modern applications means integrating with countless APIs. But what happens when those APIs are down, rate-limited, or simply don’t exist yet? Most developers resort to basic mocking tools that barely scratch the surface of real-world API complexity. API-Mocker isn’t just another mocking tool. It’s a comprehensive API development platform that has already been downloaded over 5,380 times by developers worldwide. Here’s what makes it different: FastAPI-based server with advanced routing and regex pattern matching AI-powered mock generation using OpenAI GPT models with intelligent fallback Scenario-based mocking for testing different API states and edge cases Smart response matching that analyzes request data for intelligent response selection GraphQL support with schema introspection and subscription handling WebSocket mocking for real-time communication testing Advanced authentication with OAuth2, JWT, API keys, and MFA support Database inte https://lnkd.in/gYKbM7Ku To view or add a comment, sign in API-Mocker Hits 5.38K Downloads: The Open Source API Development Platform That’s Changing How Developers Mock APIs The Problem Every Developer Faces

Building modern applications means integrating with countless APIs. But what happens when those APIs are down, rate-limited, or simply don’t exist yet? Most developers resort to basic mocking tools that barely scratch the surface of real-world API complexity. API-Mocker isn’t just another mocking tool. It’s a comprehensive API development platform that has already been downloaded over 5,380 times by developers worldwide. Here’s what makes it different: FastAPI-based server with advanced routing and regex pattern matching AI-powered mock generation using OpenAI GPT models with intelligent fallback Scenario-based mocking for testing different API states and edge cases Smart response matching that analyzes request data for intelligent response selection GraphQL support with schema introspection and subscription handling WebSocket mocking for real-time communication testing Advanced authentication with OAuth2, JWT, API keys, and MFA support Database inte https://lnkd.in/gYKbM7Ku To view or add a comment, sign in WKassebaum’s fork of zen-mcp-server seems to be better maintained than the official version, with support for more LLMs from different providers. For those unfamiliar:

zen-mcp-server is a “Model Context Protocol server that supercharges tools like Claude Code, Codex CLI, and IDE clients such as Cursor or the Claude Dev VS Code extension. Zen MCP connects your favorite AI tool to multiple AI models for enhanced code analysis, problem-solving, and collaborative development”.

https://lnkd.in/efRqQ7PH To view or add a comment, sign in The Cloudflare Code Mode approach to MCP tool calls (https://lnkd.in/erdnK7EH) sounds like a really significant improvement on the MCP experience. It’s one of those rare breakthroughs that is both elegant and obvious in hindsight.

At a high level, the idea is to translate “raw MCP” into TypeScript interfaces, and ask the LLM to code against the TypeScript interface. It’s a form of language arbitrage you might say: the agent exchanges a low-resource language (raw MCP) for a high-resource language (TypeScript), so the LLM performs much better. Then something cool happens - the LLM can also write code to chain tool calls together, or otherwise process the tool call responses in interesting ways. The agent is left holding a bunch of LLM-generated code, so it needs a sandbox to go run that code, and of course Cloudflare offers a solution for that.

We’ll see if this approach takes hold; it seems to have a lot of traction already. If it does, then it’s worth asking whether the MCP protocol itself needs a revision - for example, by making the MCP server provide the TypeScript interface natively. That then raises another round of questions, around what is the best way for MCP servers to “speak” to LLMs - can we do better than Typeface?

Certainly it’s a cool idea, and I think it’s a great step forward for MCP usage.

h/t to Kushagra Kumar for sending this post my way! To view or add a comment, sign in 💡 𝗡𝗲𝘃𝗲𝗿 𝗹𝗼𝘀𝗲 𝘁𝗿𝗮𝗰𝗸 𝗼𝗳 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗮𝗴𝗮𝗶𝗻.

Just released MCP Memory Service v8.6.0 with 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 - your personal AI-powered knowledge base.

𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: You have PDFs, documentation, notes scattered everywhere. Finding the right information takes forever. Context is lost between AI conversations.

𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Upload your documents once. Search them semantically. Let AI remember everything for you.

𝗛𝗼𝘄 𝗶𝘁 𝗪𝗼𝗿𝗸𝘀:

1️⃣ 𝗨𝗽𝗹𝗼𝗮𝗱 - Drag PDFs, docs, or notes to the web interface 2️⃣ 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 - Intelligent chunking preserves context 3️⃣ 𝗦𝗲𝗮𝗿𝗰𝗵 - Ask in natural language: “authentication flow from the security docs” 4️⃣ 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿 - AI assistants access this knowledge automatically

𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗧𝗵𝗶𝘀 𝗦𝗽𝗲𝗰𝗶𝗮𝗹:

🌟 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗦𝗲𝗮𝗿𝗰𝗵 - Finds relevant content, not just keywords 🌟 𝗣𝗿𝗶𝘃𝗮𝗰𝘆-𝗙𝗶𝗿𝘀𝘁 - Runs locally on your machine (or your team’s server) 🌟 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 - Works with Claude, VS Code, Cursor, and 13+ AI applications 🌟 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗱𝘆 - 2000+ memories in active deployments, <500ms search times

𝗕𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗧𝗲𝗮𝗺𝘀: • OAuth 2.1 collaboration • Hybrid sync (local + cloud) • Zero-configuration setup • Enterprise security

From solo developers to entire teams - one source of truth for AI-powered work.

𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 & 𝗙𝗿𝗲𝗲: 👉 https://lnkd.in/ePYekaAF

#ArtificialIntelligence #Productivity #KnowledgeManagement #DeveloperTools #OpenSource #Claude #AI To view or add a comment, sign in

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