The Tool Desk
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If your agent has many tools but uses only a few on each task, don’t load every full schema into context by default. Keep commonly used tools eager and defer the rest behind tool search, then measure whether discovery preserves task success while reducing input-token use. For a small, frequently used toolset, eager loading is often simpler.
What changes when tool schemas load eagerly or on demand?
A tool schema describes what a tool does and how an agent can call it, including its name, description, and input fields. With eager loading, the agent receives all those definitions before it handles a request. With deferred loading, it initially receives only the tools or discovery mechanism needed to find relevant definitions; a search can then return full definitions for the matches.
Eager loading is straightforward, but definitions for tools that are irrelevant to a particular request still use context. Deferred loading can reduce that initial overhead, but adds a discovery step. The agent must find the right tool from its description before it can use the full definition.
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When should you defer tool schemas?
Consider deferred loading when the catalog is large, tool definitions take a noticeable share of baseline context, and individual tasks typically need only a small subset. Anthropic’s guidance suggests considering tool search when a toolset grows past roughly 20 tools or baseline context use becomes noticeable. That is a provider recommendation, not a universal cutoff.
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Keep tools eager when the catalog is small, most tasks use the same tools, or an extra discovery call would add unnecessary complexity or latency. You can also use a hybrid: keep core tools available up front and defer less frequently used groups.
- Initial context: Deferring definitions can reduce what is sent before the request-specific tools are known.
- Task completion and selection: Search must retrieve the right definitions; unclear names or descriptions can make the relevant tool harder to find.
- Latency: Discovery may add a call or processing step. Measure end-to-end time, not just the time for the eventual tool execution.
- Implementation: Eager loading is simpler. Deferred loading requires a supported search flow and useful metadata for retrieval.
- Compatibility: The exact setup depends on the provider, API, runtime, and tool type.
How do OpenAI and Anthropic handle deferred tools?
OpenAI Responses API and MCP
In the OpenAI Responses API tool-search flow, enable tool search and mark function or MCP-server tool definitions for deferred loading. OpenAI says its search mechanism can defer tools exposed by an MCP server, a pattern it describes as most useful when the server exposes many functions. Follow the current API guide for the exact configuration supported by your integration.
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OpenAI Agents API
In the OpenAI Agents API tool-search flow, function definitions load eagerly by default. To defer selected functions, configure tool search and set defer_loading: true on those definitions. OpenAI recommends clear names and descriptions; grouping related deferred functions into namespaces or MCP servers can also help organize discovery.
Anthropic
Anthropic’s tool-search tool supports definitions marked defer_loading: true, while a search tool remains available to discover them. Search matches are expanded into full definitions so the model can call the selected tools.
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Anthropic reports that, in its described setup, tool search typically reduces tool-definition context by over 85%, loading the 3–5 tools Claude needs for a request. It also characterizes a typical multiserver setup as having about 55,000 tokens of definition overhead. These are Anthropic’s figures, with no year stated in the cited documentation; they are not independent measurements or guaranteed savings for another catalog or provider. See Anthropic’s guidance on managing tool context for its discussion of tool search, prompt caching, and context editing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you decide for your own agent?
- Establish a baseline. Record input-token use, task completion, tool-selection quality, and end-to-end latency with your current loading strategy.
- Separate core tools from the long tail. Keep tools used by most requests available eagerly; identify rarely used or task-specific tools that could be deferred.
- Check provider support. Confirm that the API or runtime supports deferred definitions for your tool type, and use its documented search configuration.
- Improve discovery metadata. Give tools distinct, descriptive names and concise explanations of when to use them. Group related tools where the provider’s implementation supports it.
- Test representative requests. Include common tasks, edge cases, and requests that need less familiar tools. Compare the same workload using eager loading and the proposed deferred or hybrid setup.
- Keep the change only if it works overall. Check that task success and tool selection remain acceptable, then weigh any input-token reduction against discovery latency and added implementation effort.
There is no established universal threshold or strategy across providers in the cited documentation. Anthropic’s roughly 20-tool suggestion is a useful prompt to evaluate context use, not a rule. The official guidance provides implementation advice and vendor-reported figures, not a controlled, independent head-to-head benchmark.
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