Agents aren't the problem – Existing systems and API's were not built for AI
The article discusses the challenges of making existing enterprise APIs compatible with AI agents. It highlights the limitations of traditional search methods and introduces a hybrid approach that combines different search techniques. The solution, called AI Enrichment, enhances API metadata to improve agent understanding without altering the original API structure.
- ▪Existing enterprise APIs often have names and descriptions that are not suitable for AI interpretation.
- ▪A hybrid search method was developed to improve the accuracy of tool discovery for AI agents.
- ▪AI Enrichment allows for clearer naming and descriptions of API tools while preserving the original schema.
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backbackPlatform•May 26, 2026MCP Bridge Part 3: How we made getProcInfo3() agent-readable: hybrid discovery + AI EnrichmentIn the previous article, we walked through Code Mode, three meta-tools that replace the entire MCP tool catalog when the API surface is large. The first of those three meta-tools is search_tools. Today we're opening it up.search_tools is what stands between an LLM agent and a 200-operation API surface. It needs to take a natural-language description of what the agent wants to do, and return the three or four tools that can actually do it. Get this wrong and the agent ends up either flailing through irrelevant tools or, worse, calling the wrong one confidently.We thought this would be the easy part of MCP Bridge.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Appfactor.
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