Plexara
The cost of a long tool list
Every tool an agent can call carries a description and a parameter schema, and those definitions are part of the context the model reads before it answers. A handful of tools is free. A few dozen is a tax the agent pays on every single turn, whether or not any of them are relevant to the question.
The cost is not only tokens. A long list degrades judgment. Faced with dozens of similar-sounding tools, an agent picks the wrong one more often, calls it with the wrong arguments, and burns a round trip discovering the mistake. Past the point where the agent can hold them all in view, more options tend to produce worse decisions.
Finding the right tool by intent
Plexara lets the agent locate the best tool for a request by intent rather than by scanning a flat list. The agent describes what it is trying to do, and the platform returns the tool that matches that meaning, even when the wording does not line up with the tool name.
This keeps the working set small without making the platform small. The full breadth of connections and operations stays available, but the agent does not have to carry all of it at once. It reaches for the right capability when the task calls for it, which is how a person uses a large toolbox without memorizing every drawer.
The result is that the agent spends less of each conversation deciding what to use and more of it doing the work.
Memory that carries across sessions
Efficiency within a single conversation is only half the problem. Without memory, every session starts cold. The agent relearns the same preferences, rediscovers the same corrections, and reestablishes the same context it had yesterday, paying for that ramp-up every time.
Plexara recall blends meaning and keywords, so the agent brings back the context that actually relates to the current question rather than the most recent or most literal match. Preferences, corrections, and prior findings carry across sessions for both agents and analysts.
The result is an assistant that accumulates rather than resets. A correction made once stays made. Context established in one session is available in the next, so the work compounds instead of repeating.
Efficiency as a feature, not an accident
It is tempting to treat tool selection and memory as plumbing. In practice they are what separate an assistant that feels sluggish and forgetful from one that feels sharp. The user does not see the search index or the recall strategy. They feel the difference as answers that arrive faster and a system that remembers what they told it.
Selecting by intent and remembering across sessions are deliberate design choices with a single aim: an agent whose cognitive load tracks the task in front of it, however large the platform behind it grows.
Further Reading
RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation
Tiantian Gan, Qiyao Sun · 2025
Shows that loading all tool definitions into the prompt causes 'prompt bloat' and that baseline tool-selection accuracy collapses to 13.62% with a large tool pool, rising to 43.13% when semantic retrieval pre-filters tools, while cutting prompt tokens by over 50%.
LongFuncEval: Measuring the effectiveness of long context models for function calling
Kiran Kate, Tejaswini Pedapati, Kinjal Basu, Yara Rizk, Vijil Chenthamarakshan, Subhajit Chaudhury, Mayank Agarwal, Ibrahim Abdelaziz · 2025
Empirically demonstrates that LLM function-calling accuracy deteriorates significantly as the number of available tools and the resulting context length grow, with position bias becoming more pronounced.
Domain-specific Question Answering with Hybrid Search
Dewang Sultania, Zhaoyu Lu, Twisha Naik, Franck Dernoncourt, et al. · 2024
Reports that a hybrid approach combining a dense (semantic) retriever with keyword-based sparse search reaches nDCG 0.847, outperforming the dense retriever alone (0.828) and keyword/BM25 alone (0.640).