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Tool design guide / 2026

Designing AI Agent Tool Calls for Search and Retrieval in 2026

An agent performs better when tools reflect intent instead of exposing a grab bag of transport details. Clear names, validated inputs, predictable shapes, and explicit scope make behavior easier to evaluate.

Quick answer

Make each tool answer one retrieval question.

Use search for “find sources,” extraction for “read this URL,” news for “what is recent,” and finance for a current market snapshot.

curl --get 'https://extractor.sh/api/extract' \
  --data-urlencode 'url=https://example.com/' \
  --data-urlencode 'format=json'

Available data

What you can extract

  • Purpose-built tool inputs
  • Markdown and JSON representations
  • Shared cache and rate-limit behavior
  • Source-linked output

AI workflows

Where normalized data helps

  • MCP client integrations
  • Function-calling chatbots
  • Agent evaluation harnesses
  • Developer tooling assistants

AI-ready output

Markdown for models. JSON for systems.

Raw HTML consumes tokens on navigation, scripts, styling, and interface labels. Clean Markdown keeps the readable hierarchy for LLM prompts and RAG chunks. Normalized JSON is better when your application needs an explicit semantic type, source, author, publication date, media, attributes, and collection items.

Always retain the canonical URL from the response. AI-generated summaries should remain traceable to the public source, especially when the underlying page can change.

Boundaries

Public data only

  • Tools cannot resolve ambiguous intent without a clear assumption.
  • Do not expose credentials through retrieval tools.
  • No tool promises restricted-source access.

extractor.sh does not bypass CAPTCHAs, login walls, paywalls, access controls, or regional restrictions. Review the source’s terms and applicable law before collecting or reusing data.