AI
The Complete Guide to Tool Selection in AI Agents
The article by Shittu Olumide, published on July 6, 2026, addresses the degradation of AI agent accuracy as tool catalogs grow, presenting six techniques
Key takeaways
- Agent accuracy degrades as tool catalogs grow, not just due to slower responses but due to tool hallucination and accuracy loss.
- The RAG-MCP framework triples tool selection accuracy (from 13.62% to 43.13%) and cuts prompt tokens by over 50% by using semantic retrieval to filter tools before the LLM sees them.
- Gating, retrieval, routing, and planning narrow down what the model sees before it chooses a tool, improving accuracy without requiring a bigger model.
- Fallback logic and benchmarking measure whether fixes actually work, ensuring agents degrade gracefully rather than crash.
- The techniques are layers: gating filters turns needing no tools, retrieval/routing narrows the catalog, planning sequences multi-step tasks, and fallback logic handles ambiguous queries.
The article by Shittu Olumide, published on July 6, 2026, addresses the degradation of AI agent accuracy as tool catalogs grow, presenting six techniques to maintain accuracy and efficiency: gating, retrieval, routing, planning, fallback logic, and benchmarking. It highlights research (e.g., the RAG-MCP paper from May 2025) showing that tool selection accuracy triples (from 13.62% to 43.13%) and prompt tokens are cut by over 50% when retrieval filters the tool catalog before the LLM sees it. The article includes practical code examples (retriever, router, planner, fallback logic) and a benchmark harness to measure improvements, emphasizing that these techniques do not require larger models but smarter tool list design.
By the numbers
- 5
- initial tools in agent demo
- 40
- tools in agent after three months
- 10-15
- tool count threshold for accuracy degradation
- 13.62%
- tool selection accuracy with full catalog exposed
- 43.13%
- tool selection accuracy with retrieval-filtered selection
- -50%
- reduction in prompt tokens with retrieval-filtered selection
How it unfolded
- RAG-MCP paper published
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Common questions
- What happened with RAG-MCP?
- Agent accuracy degrades as tool catalogs grow, not just due to slower responses but due to tool hallucination and accuracy loss.
- Where can I read the original report?
- Read the full report at ml_mastery.
