Semantic search across MCP tools with Amazon Bedrock AgentCore Gateway

2026-05-24 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Intermediate, quick

Summary

Amazon Bedrock AgentCore Gateway introduces a solution for scaling large language model agents that interact with numerous tools. Traditional agents struggle with hundreds of tools due to context window limitations and decreased tool-selection accuracy. The Gateway addresses this by integrating built-in semantic search across all hosted tool descriptions. It maintains embeddings of each tool, allowing clients to query this index with natural language to identify the most relevant tools before invocation. This two-stage selection process results in shorter prompts, reduced token costs, and improved tool-selection accuracy, making it feasible to manage agents with five hundred tools effectively. The full source code is available in the "amazon-bedrock-agentcore-samples" repository.

Key takeaway

For AI Architects designing large-scale agent systems, Amazon Bedrock AgentCore Gateway offers a critical pattern for managing tool complexity. You should consider implementing its built-in semantic search to overcome context window limits and improve tool selection accuracy when your agents interact with dozens or hundreds of tools. This approach reduces token costs and simplifies prompt engineering, ensuring your agent deployments remain performant and cost-effective.

Key insights

Amazon Bedrock AgentCore Gateway enables scalable LLM agents through built-in semantic search for tool selection.

Principles

• Agents with many tools (e.g., five hundred) fail without a two-stage selection process.
• Co-locating the tool index with the endpoint improves latency and freshness.

Method

The Gateway maintains tool description embeddings, clients query the index, and the agent loop uses top-k search results to populate its working tool list.

In practice

• Build a Gateway hosting dozens of MCP tools.
• Implement a semantic search query for top-k tools.
• Integrate search results into an agent's tool list.

Topics

• Amazon Bedrock
• AgentCore Gateway
• Semantic Search
• LLM Agents
• Tool Selection
• Vector Embeddings

Code references

• awslabs/amazon-bedrock-agentcore-samples

Best for: AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.