u/Dull_Purple_3606

I’ve been experimenting with Claude + Model Context Protocol (MCP), and the most interesting part isn’t that MCP lets Claude “connect to more tools.”

It’s that you can turn a collection of disconnected tools into something that feels like an actual workflow.

For example, instead of asking Claude:

“What’s in my project folder?”

you can build an MCP workflow where Claude can:

read files → inspect a database → query an API → analyze the results → create an output

without having to manually copy/paste information between systems.

A simple mental model

Think of MCP as a standardized bridge:

Claude

│

MCP Client

│

┌────────┼────────┐

│ │ │

Files Database API

Server Server Server

│ │ │

Project SQL External

files data service

The important distinction is that MCP isn't the automation itself.

MCP provides a standardized way for an AI application to discover and use capabilities exposed by an MCP server.

The interesting stuff happens when you design those capabilities around an actual job.

A practical example: debugging a project

Imagine an MCP server exposing tools like:

get_project_files()

read_file(path)

search_code(query)

run_tests()

get_test_output()

Now Claude can potentially work through something closer to a real debugging loop:

1. Inspect project structure

2. Find relevant code

3. Read the implementation

4. Run tests

5. Analyze failure

6. Identify likely cause

7. Propose a change

8. Run tests again

That's much more useful than simply giving an LLM access to “your entire filesystem.”

The lesson I’m taking away

Tool quantity matters much less than tool design.

Giving an agent 50 poorly scoped tools can actually make a workflow worse.

A smaller MCP server with 5–10 well-designed tools can be dramatically easier for an AI to reason about.

For example, this:

execute_sql(query)

is extremely flexible.

But something like:

get_customer_orders(customer_id)

gives the model a much clearer capability with less room for accidental misuse.

Where MCP gets really interesting

I've found these patterns particularly interesting:

1. Internal knowledge workflows

Instead of building a custom integration for every AI application:

Claude

↓

MCP

↓

Company knowledge / docs / databases

you can potentially expose the same capabilities to different MCP-compatible clients.

2. Developer workflows

Git repositories, issue trackers, test systems, CI information and documentation can become part of the same reasoning loop.

3. Data analysis

An MCP server can expose carefully controlled database operations instead of dumping an entire database into the model's context.

4. Automation

This is where I think people need to be careful.

There's a big difference between:

AI → read information

and:

AI → send email

AI → modify production data

AI → deploy code

AI → delete records

The second category needs significantly stronger permission boundaries, confirmation steps, logging and auditing.

One design rule I'd recommend

Design MCP tools around outcomes, not underlying APIs.

Instead of exposing 30 raw API endpoints:

create_x()

update_x()

get_x()

delete_x()

list_x()

...

ask:

“What is the useful task the agent actually needs to accomplish?”

Then expose a smaller set of high-level operations where appropriate.

That makes the system easier for both humans and models to understand.

My current MCP checklist

When building or evaluating an MCP server, I'd ask:

What can the server read?

What can it change?

Which actions are destructive?

Are permissions scoped appropriately?

Can every important action be logged?

Are tool descriptions clear enough for an LLM?

Are tools too generic?

Can the workflow be tested without giving the model unrestricted access?

What happens when the model makes the wrong tool call?

Is there a human confirmation step for high-impact actions?

The last few questions are probably more important than how many integrations you have.

Curious what others are building

What has been your most useful real-world MCP workflow so far?

I'm especially interested in setups where MCP actually saved time or eliminated a bunch of manual context switching—not just demos where Claude can technically call an API.

If you share your setup, I'd also love to hear what didn't work. Those failures are often more useful than the successful demos.

I'll try to reply to comments with implementation ideas, trade-offs, or alternative approaches where I can.

I’m currently trying to figure out which direction to pursue on the sell side, and honestly, it’s hard to get a realistic picture of what the different roles are actually like.

You hear about IB, equity research, S&T, corporate banking, etc., but most of the information online is either very generic or focused on recruiting.

For people actually working in these roles:

Investment Banking — how much of your time is really modeling/deal work vs. decks, admin, and client requests?

Equity Research — what’s the day-to-day like outside of earnings season? How much research/writing vs. modeling?

Sales & Trading — how different are sales, trading, and structuring in terms of lifestyle and career progression?

Corporate Banking — how does the work compare with IB? Is it mostly credit/relationship management, or do you still get meaningful exposure to deals?

Other sell-side roles — anything underrated that people considering finance should know about?

I’m also curious about the things you don’t usually see in career guides:

Which jobs have the best WLB?

Which ones are actually interesting after the first few years?

How important are school/brand names once you’re in?

What are the realistic exit opportunities?

Which roles have the biggest gap between what you expected and what the job actually turned out to be?

If you could start over, would you choose the same path?

Would be really interested in hearing from people at different levels, especially those who have switched between these areas.

Not looking for the usual “IB pays more but has worse hours” answer — more interested in what the jobs are actually like day to day and what you’ve learned from doing them.

I’ve been messing around with MCP (Model Context Protocol) lately, and I’m starting to realize that the fun part isn’t just getting Claude to “use tools.”

It’s figuring out which integrations are actually worth setting up.

For example, connecting Claude to:

GitHub → “Check this repo and tell me what’s going on.”

Notion/Google Drive → “Find the doc where we discussed this.”

PostgreSQL → “Why did sales drop last month?”

Slack → “Find the conversation about this bug.”

Jira/Linear → “What tickets are blocking this release?”

Local files → “Go through these files and figure out what I broke.” 😅

Custom APIs → basically unlimited possibilities if you’re willing to build it.

The part I’m still figuring out is how much access to give Claude.

Giving an MCP server access to your database or internal tools is obviously pretty powerful, but also... maybe not something you want to casually connect to everything.

So I’m curious what everyone else is doing.

What’s your “I can’t go back now” MCP integration?

And if you’ve built your own MCP server, what ended up being the biggest headache — auth, permissions, tool design, context, or just getting Claude to actually use the tools correctly?

Would love to hear what people are actually using in real workflows rather than just the usual list of MCP demos.

Hey everyone! 👋

We’re currently looking for a few UGC creators to work with us on some upcoming projects.

We’re mainly looking for people who can make content that feels natural, relatable, and not overly “salesy.” You don’t need to have a huge following — we care more about how comfortable you are on camera and the quality of your content.

We’re looking for creators who can do things like:

Short-form UGC videos

Talking-head videos

Product demos

Reviews/testimonials

Unboxings

Lifestyle-style content

TikTok/Reels-style videos

A few things we’re looking for:

Comfortable speaking on camera

Can follow a simple brief or script

Good communication

Reliable with deadlines

Open to reasonable feedback/revisions

Beginner creators are welcome if you have good examples to share

This is paid work. Rates will depend on the project, number of videos, and usage requirements.

If you’re interested, send us a DM with your portfolio/examples, rates, location, and availability. If you don’t have a formal portfolio yet, feel free to send a few sample videos.

We’ll take a look and reach out if you’re a good fit for one of our upcoming projects.

Looking forward to seeing what you guys create! 🙌

I’m trying to become more financially responsible and build some stability for myself, and I’ve realized that a lot of financial lessons only really make sense after you’ve experienced them.

So I’m curious:

What’s one money mistake you made when you were younger that you would absolutely avoid today?

It could be anything—credit cards, loans, lifestyle inflation, buying things you couldn't afford, not saving, choosing the wrong career, trusting someone financially, or even just not learning about money sooner.

And on the other side:

What’s one financial decision you made that genuinely improved your life?

I’m hoping this can be one of those comment sections where people share real experiences instead of just generic “save more money” advice.

I’ll definitely be reading the replies. There might be a lesson here that someone else really needs to hear.