Tool combinations

Anthropic-provided tools are designed to work together. Common agent patterns pair tools that cover complementary stages of a workflow: one tool gathers or discovers, another processes or acts. The combinations below are starting points, not prescriptions. Mix them to fit your task.

Each snippet shows only the tools array. See Handle tool calls for the full request shape.

Research agent: web_search + code_execution

Search finds sources; code execution analyzes and synthesizes. Claude searches for data, then writes Python to process, tabulate, or visualize it. This pairing is a good fit for questions that require both up-to-date information and nontrivial computation over that information, such as "compare this quarter's earnings across the top five cloud providers."

{
  "tools": [
    { "type": "web_search_20260209", "name": "web_search" },
    { "type": "code_execution_20250825", "name": "code_execution" }
  ]
}

The flow is typically search, then execute, then optionally search again if the first pass surfaced a gap. Code execution runs server-side, so there's no client-side sandbox to manage.

Coding agent: text_editor + bash

The text editor reads and modifies files; bash runs tests and build commands. This is the canonical software-development loop: inspect the code, make an edit, run the tests, repeat. Both tools are client-executed, so your application controls which files and commands are accessible.

{
  "tools": [
    { "type": "text_editor_20250728", "name": "str_replace_based_edit_tool" },
    { "type": "bash_20250124", "name": "bash" }
  ]
}

Pair this with a constrained working directory and a command allowlist if the agent operates on untrusted code. See Text editor tool and Bash tool for the execution contracts.

Cite-then-fetch: web_search + web_fetch

Search surfaces candidate URLs; fetch retrieves full page content for the relevant ones. This avoids fetching everything upfront. Claude runs a search, inspects the snippets, picks the two or three results that actually look relevant, and fetches only those.

{
  "tools": [
    { "type": "web_search_20260209", "name": "web_search" },
    { "type": "web_fetch_20260209", "name": "web_fetch" }
  ]
}

This pairing is useful when the answer lives in long-form content (documentation pages, articles, specifications) that a search snippet can't fully capture. Fetch pulls the complete page so Claude can cite specific passages.

Long-running agent: memory + any toolset

Memory persists state across conversations; the other tools do the work. Add memory to any agent that needs to remember prior sessions, such as a support agent that recalls a customer's earlier issues or a project assistant that tracks decisions made last week.

{
  "tools": [{ "type": "memory_20250818", "name": "memory" }]
}

Add your other tools alongside memory in the same array.

Memory is orthogonal to the rest of your toolset. It doesn't change how other tools behave; it gives Claude a place to write down and later retrieve facts that would otherwise be lost when the context window resets. See Memory tool for the storage model.

All-in-one: computer_use

The computer use tool subsumes most others by operating a full desktop. Claude sees screenshots and issues mouse and keyboard actions, which means it can drive any application a human can. Use this when the task requires arbitrary GUI interaction that more specific tools can't reach: legacy software without an API, visual verification steps, or workflows that span multiple desktop apps.

{
  "tools": [
    {
      "type": "computer_20250124",
      "name": "computer",
      "display_width_px": 1280,
      "display_height_px": 800
    }
  ]
}

Computer use is the most general option and also the slowest, since every action requires a screenshot roundtrip. Prefer narrower tools when they cover your use case, and reach for computer use when nothing else fits. See Computer use tool for the sandbox setup.

Next steps