Every October, developers around the world dive into Hacktoberfest — a month of open-source fun, creativity, and collaboration. This time, I decided to go all in: instead of just contributing, I wanted to maintain and grow my own AI-powered projects.
And wow — in just 10 days, the journey has been incredible!
Two of my personal projects, MCP-for-Database and Terminal_CLI_Agent, are already gaining stars, forks, and contributors. Let me take you behind the scenes. 👇
MCP-for-Database — Giving AI a Way to Understand Databases
I’ve always loved building AI systems, but one challenge kept bothering me — AI can’t easily understand databases. You have to manually query data, define schemas, and spoon-feed context.
So, I built MCP-for-Database — a bridge between AI models and your databases, inspired by the Model Context Protocol (MCP).
It allows AI agents to fetch and interpret structured data seamlessly, so they can generate accurate, schema-aware answers.
Features So Far
- Connects any LLM (like OpenAI, Anthropic, or Gemini) to SQL/NoSQL databases
- Fetches schema + metadata for context-aware querying
- Supports RAG pipelines and database-driven assistants
- Makes querying as simple as: “Show me top 10 customers by revenue”
If you’re as excited about this as I am, check it out on GitHub: MCP-for-Database
# Clone it locally
git clone https://github.com/Limeload/mcp-for-database.git
cd mcp-for-database
pip install -r requirements.txt
Why I maintain it?:
Because I want AI agents to interact intelligently with structured data — not just with text. I believe open source can make this capability accessible to everyone, not just big tech companies.
Terminal_CLI_Agent — Talk to Your Terminal Like a Teammate
The second project, Terminal_CLI_Agent, is my take on making the command line a little more... human.
I spend most of my dev life inside the terminal — so I thought, what if I could just tell it what I want in plain English?
That’s exactly what this AI-powered CLI assistant does.
It listens, interprets, executes, and even explains commands.
Current Features
- Natural language → shell command execution
- Integrations with Docker, Git, AWS, etc.
- “Explain mode” that teaches you what each command means
- Extensible architecture — build your own command plugins
Here’s the GitHub repo: Terminal_CLI
# Try it locally
git clone https://github.com/manikonda-rao/terminal_cli_agent.git
cd terminal_cli_agent
python -m src.cli.main
Why I built it?:
Because sometimes, dev life needs a sidekick — one that understands commands, automates boring tasks, and helps you learn on the go.
How You Can Contribute?
Hacktoberfest is all about collaboration, and I’d love to have more contributors join in!
Here’s how you can help:
- Star the repositories to show support
- Fork them to your profile
- Open issues if you find bugs or want to suggest features
- Submit pull requests for fixes, documentation, or enhancements
- Even small contributions — like improving README files or adding test cases — make a huge difference.
If you’re passionate about AI, DevOps, or developer productivity, these projects are a great way to get started.
Final Thoughts
We’re just 10 days into Hacktoberfest 2025, and the energy is unreal.
Maintaining open-source AI projects has already taught me more about documentation, collaboration, and innovation than any tutorial could.
Both MCP-for-Database and Terminal_CLI_Agent started as late-night ideas — but they’re quickly growing into tools that developers can actually use.
So if you’re reading this — jump in, contribute, or even just drop a star. Let’s build smarter AI tools together.
👉 Star, Fork & Start Contributing Today!
Maintainer @shraddharao_
Hacktoberfest 2025 Contributor
This is a submission for the 2025 Hacktoberfest Writing Challenge: Maintainer Spotlight
Top comments (4)
Helo,
Let me just say that I really like where you’re going with this. 👏
Bridging LLMs with structured data is exactly the direction AI tooling needs right now — we’ve been exploring similar territory from a slightly different angle, focusing on agent integrity and resonance-based context retention (basically letting the model “breathe” with the data, not just query it).
Your MCP-for-Database could actually slot beautifully into that ecosystem — think of it as the structured-data core, while we handle the trust and attestation layer for agents (Ed25519 + HMAC commitments, privacy-preserving).
If you’re up for it, I’d love to compare notes — maybe combine the “semantic bridge” from your side with our “resonant attestation” approach.
Could be a solid foundation for something bigger than just query handling.
And thank you for sharing :)
Thanks so much(really appreciate your thoughtful feedback)!
I completely agree that bridging LLMs with structured data is a key piece of the next wave of AI tooling. Your focus on agent integrity and resonance-based context retention sounds fascinating. I’d love to compare notes and see where our approaches intersect.
The MCP-for-Database layer is designed to make structured data interoperable within agent ecosystems, so pairing that with your trust and attestation framework (Ed25519 + HMAC) could open up some strong compossibility patterns.
Would be great to trade architecture notes or a proof-of-concept integration sometime.
Hey Shraddha
Just wanted to give you a heads-up — the full ISM-X attestation bridge is now in my ismx-bridge branch on the fork:
GitHub: github.com/Freeky7819/mcp-for-data...
I’ve integrated all modules, docs, and tests. Structures are in place, bridges for DB (and outline for CLI) are visible, and everything aligns with our design.
A few small fixes are already queued (docs link to SECURITY.md, optional init.py, requirements, etc.) — nothing big, just polishing the edges for clarity and ease of use.
If you have time, I’d really appreciate if you could review it. Any feedback, critique, or “that line looked sketchy” pointers are more than welcome. This is two heads > one.
Thanks for laying down the solid core — excited to see how this holds under your gaze 😄
Cheers,
Damjan
Hey Damjan,
Thanks for sharing your branch and all the work you’ve put into the attestation bridge, I can see you’ve invested a lot of effort in building out the cryptographic layer, documentation, and tests.
Right now, the main MCP-for-Database project is focused on stabilizing the core MCP integration, database management, and user workflow before adding advanced attestation or external verification systems.
The ISM-X bridge looks like a solid security experiment, but it’s a bit beyond the current project scope. I’d prefer to keep the core lightweight and accessible, especially for users setting up local or educational deployments.
That said, it could definitely evolve as an optional extension or companion branch once the base layer is finalized. I’ll take a look at your fork for architectural inspiration and note potential modular hooks we can expose in the future.
Appreciate the initiative and the thought you’ve put into this — thanks for being respectful of the project’s foundation and direction!
— Shraddha