Honestly vetting MCP seems like a YC company in and of itself.
> A natural reaction is to design a dynamic action space—perhaps loading tools on demand using something RAG-like. We tried that in Manus too. But our experiments suggest a clear rule: unless absolutely necessary, avoid dynamically adding or removing tools mid-iteration. There are two main reasons for this:
> 1. In most LLMs, tool definitions live near the front of the context after serialization, typically before or after the system prompt. So any change will invalidate the KV-cache for all subsequent actions and observations.
> 2. When previous actions and observations still refer to tools that are no longer defined in the current context, the model gets confused. Without constrained decoding, this often leads to schema violations or hallucinated actions.
> To solve this while still improving action selection, Manus uses a context-aware state machine to manage tool availability. Rather than removing tools, it masks the token logits during decoding to prevent (or enforce) the selection of certain actions based on the current context.
Strata's architecture is philosophically different. Instead of loading a large toolset and masking it, we guide the LLM through a multi-step dialogue. Each step (e.g., choosing an app, then a category) is a separate, very small, and cheap LLM call.
So, we trade one massive prompt for a few tiny ones. This avoids the KV-cache issue because the context for each decision is minimal, and it prevents model confusion because the agent only ever sees the tools relevant to its current step. It's a different path to the same goal: making the agent smarter by not overwhelming it. Thanks for the great link!
https://docs.klavis.ai/documentation/quickstart#open-source
Add a call to the mintlify cli ‘mint broken-links’ into your CI and you should be set!
2. Yes, i see, this is what i meant by agentic search. Essentially is a tiny subagent, taking list of tools in and out the relevant ones. Still implementable in 5 mins. But i guess if the experience is very smooth enterprise might pay?
2. Yes the idea is not complex once you understand it. But there are some nuances we found along the way and supporting more integrations are always important but requires engineering efforts. Thank you!