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You can look at AnyTool, 2024 (16,000 tools) and start looking at newer research from there. https://arxiv.org/abs/2402.04253 https://arxiv.org/abs/2402.04253

by storus 1y ago

You can look at AnyTool, 2024 (16,000 tools) and start looking at newer research from there. https://arxiv.org/abs/2402.04253 https://arxiv.org/abs/2402.04253 For long contexts start with activation beacons and RoPE scaling.

simonw 1y ago
I would classify AnyTool as a context engineering trick. It's using GPT-4 function calls (what we would call tool calls today) to find the best tools for the current job based on a 3-level hierarchy search. Drew calls that one "Tool Loadout" https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html#tool-loadout https://www.dbreunig.com/2025/06/26/how-to-fix-your-context....
- timr 1y ago
  So great. We have not one, but two different ways of saying "use text search to find tools". This field, I swear...it's the PPAP [1] of engineering. [1] https://www.youtube.com/watch?v=NfuiB52K7X8 https://www.youtube.com/watch?v=NfuiB52K7X8 I have a toool...I have a seeeeearch...unh! Now I have a Tool Loadout!" *dances around in leopard print pyjamas*
nyrikki 1y ago
Thanks for the link. It finally explained why I was getting hit up by recruiters for a job that was for a data broker looking to do what seemed like silly uses. Cloud API recommender systems must seem like a gift to that industry. Not my area anyways but I couldn't see a profit model for a human search for an API when what they wanted is well covered by most core libraries in Python etc...
Art9681 1y ago
RoPE scaling is not an ideal solution since all LLMs in general start degrading at around 8k. You also have the problem of cost by yolo'ing long context per task turn even if the LLM were capable of crunching 1M tokens. If you self host then you have the problem of prompt processing time. So it doesnt matter in the end if the problem is solved and we can invoke n number of tools per task turn. It will be a quick way to become poor as long as providers are charging per token. The only viable solution is to use a smart router so only the relevant tools and their descriptions are appended to the context per task turn.

by storus 1y ago

simonw 1y ago

I would classify AnyTool as a context engineering trick. It's using GPT-4 function calls (what we would call tool calls today) to find the best tools for the current job based on a 3-level hierarchy search. Drew calls that one "Tool Loadout" https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html#tool-loadout https://www.dbreunig.com/2025/06/26/how-to-fix-your-context....

timr 1y ago
So great. We have not one, but two different ways of saying "use text search to find tools". This field, I swear...it's the PPAP [1] of engineering. [1] https://www.youtube.com/watch?v=NfuiB52K7X8 https://www.youtube.com/watch?v=NfuiB52K7X8 I have a toool...I have a seeeeearch...unh! Now I have a Tool Loadout!" *dances around in leopard print pyjamas*

nyrikki 1y ago

Thanks for the link. It finally explained why I was getting hit up by recruiters for a job that was for a data broker looking to do what seemed like silly uses. Cloud API recommender systems must seem like a gift to that industry. Not my area anyways but I couldn't see a profit model for a human search for an API when what they wanted is well covered by most core libraries in Python etc...

Art9681 1y ago

RoPE scaling is not an ideal solution since all LLMs in general start degrading at around 8k. You also have the problem of cost by yolo'ing long context per task turn even if the LLM were capable of crunching 1M tokens. If you self host then you have the problem of prompt processing time. So it doesnt matter in the end if the problem is solved and we can invoke n number of tools per task turn. It will be a quick way to become poor as long as providers are charging per token. The only viable solution is to use a smart router so only the relevant tools and their descriptions are appended to the context per task turn.