fixed typos, some em-dashes from this proof read model *cough* mistral *cough*

Author: madaboutcode

_includes/css/main-new.css.map

@@ -15,9 +15,9 @@ A recent [blog post by David Mohl](https://david.coffee/i-still-prefer-mcp-over-
 
 Think about what happens when you give an AI agent an MCP for Jira. The MCP works - the agent can read your tickets, search, update, add comments. Which is cool. But then you ask it something like "hey, I think there's a ticket where this teammate left a comment about the deployment issue." And now the agent has to load up all your tickets, go through every one of them, pull all the details for each ticket, before it can find that one comment from that one person. All of that costs you tokens. Your context window is now filled with junk because it loaded like 9 unwanted tickets and you haven't even started your actual work on reproducing the issue. 
 
-You know what this reminds me off? Good old GUIs. If you're anything like me, you find GUIs slow, limiting, and kind of annoying. You're in Jira, and you want to compare two tickets - you open them side by side or switch between tabs. You want to find all tickets mentioning a specific idea, you end up clicking through each one. It's convoluted, and you can only do what the UI lets you do. If you have the API, you can write a script that automates the whole thing. (Yes, the script might take two hours to write for something that takes ten minutes to click through. But we both know which one you'd rather have on Monday morning.)
+You know what this reminds me of? Good old GUIs. If you're anything like me, you find GUIs slow, limiting, and kind of annoying. You're in Jira, and you want to compare two tickets - you open them side by side or switch between tabs. You want to find all tickets mentioning a specific idea, you end up clicking through each one. It's convoluted, and you can only do what the UI lets you do. If you have the API, you can write a script that automates the whole thing. (Yes, the script might take two hours to write for something that takes ten minutes to click through. But we both know which one you'd rather have on Monday morning.)
 
-At least the Jira UI lets you save a filter. The agent gets none of that. You can't save any of this for next time either. Same thing tomorrow. Same cost. Why would you do that when a script gives you the right answer every time and runs in milliseconds? Why trade repeatability, consistency and the knowledge pre-baked in the script for something like text with instructions that the Agent may or maynot follow? 
+At least the Jira UI lets you save a filter. The agent gets none of that. You can't save any of this for next time either. Same thing tomorrow. Same cost. Why would you do that when a script gives you the right answer every time and runs in milliseconds? Why trade repeatability, consistency and the knowledge pre-baked in the script for something like text with instructions that the Agent may or may not follow? 
 
 A developer would have just written a script. Hit the API, filter by commenter, done. Works forever. And if you let the AI agent work the same way, it can do the same thing - build a custom tool, take the Jira APIs, put together exactly what it needs for the kind of work you do. MCP takes that away.
 
@@ -45,7 +45,7 @@ jirapi "/search?jql=assignee=currentUser()&fields=id,key,summary,comment" | \
 
 Clean JSON list. Few hundred bytes instead of your entire Jira instance loaded into context. And you can pipe that into whatever's next.
 
-**Token cost** -  Every MCP server you connect loads its tool definitions into context. Anthropic themselves [published numbers on this](https://www.anthropic.com/engineering/advanced-tool-use) — a five-server setup can hit 58 tools consuming around 55K tokens before the conversation even starts. Before the agent has done anything useful. And every intermediate result from every MCP call piles up too. You're paying for it in money, and the agent has less room to actually think about your problem.
+**Token cost** -  Every MCP server you connect loads its tool definitions into context. Anthropic themselves [published numbers on this](https://www.anthropic.com/engineering/advanced-tool-use) - a five-server setup can hit 58 tools consuming around 55K tokens before the conversation even starts. Before the agent has done anything useful. And every intermediate result from every MCP call piles up too. You're paying for it in money, and the agent has less room to actually think about your problem.
 
 **Reusability** - Even Jira's own UI lets you save a query and run it again. Developers build dashboards that show exactly what they need every morning. MCP gives the agent none of this. Every session starts from zero. You can try keeping a query around, sure, but that's going half the way. You can probably do a half-assed job with saved queries, but the real power is a script that does exactly what you need, every time, without burning inference. The script sticks around. The MCP interaction doesn't.
 
@@ -108,8 +108,8 @@ I use [uv](https://github.com/astral-sh/uv) for my Python scripts, with inline d
 
 And here's a trick that's been paying off: occasionally look at how the agent is actually using your skills. Have it audit its own patterns - what's it rebuilding every session that should already be a script? Build that script, add it to the skill. More usage, better tools, less wasted inference. It compounds.
 
-Reach for MCP where the constrained interface genuinely matches what you need — consumer surfaces, non-technical users. Use it as the GUI it is.
+Reach for MCP where the constrained interface genuinely matches what you need - consumer surfaces, non-technical users. Use it as the GUI it is.
 
-This is a new paradigm for all of us. We're discovering things as we go along — there are no right or wrong answers, and things change in a matter of weeks. In every corner of the world, developers and even technically inclined users are trying out different workflows. The AI companies are shipping features faster than we can keep up — like a caffeinated squirrel coding in prod (and sometimes leaking their own source code to keep the legal team busy). Tooling will keep evolving. We'll see better solutions that address the limitations of MCP and skills, scripts, and everything else — or entirely new paradigms that expose the same functionality differently.
+Nobody has this figured out yet. We're discovering things as we go along - there are no right or wrong answers, and things change in a matter of weeks. In every corner of the world, developers and even technically inclined users are trying out different workflows. The AI companies are shipping features like prod is just another dev machine (and sometimes leaking their own source code to keep the legal team busy). Things will keep evolving. We'll see better tooling that addresses the limitations of MCP and skills, scripts, and everything else - or something we haven't even thought of yet.
 
-I'm excited for what tomorrow brings. Hopefully I'll still have a job.
+Can't wait to open Hacker News or Reddit tomorrow and feel excited about the 10 new features and 4 new model releases. Hopefully I'll still have a job.