The Tool Desk
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Diagnose what is consuming context
Context pressure often comes from two different parts of tool use: the definitions the model can see and the results accumulated as it works. Anthropic’s documentation summarizes the issue: “Tool definitions and accumulated tool_result blocks consume your context window.” A large tool catalog can burden the prompt before the task begins; long results can keep adding weight as the conversation proceeds.
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- Definitions: Tool names, descriptions, and schemas available to the model.
- Intermediate results: Data returned by tools and included in the model-visible conversation.
- Stale history: Earlier messages or results that no longer help with the current step.
These pressures call for different remedies. Caching a repeated prompt may reduce the cost of sending stable input, for example, but it does not make the input contain fewer context tokens.
Match the control to the pressure
| Problem | Control | What changes | Trade-off |
|---|---|---|---|
| Many tool definitions are available, but few are relevant to a given request. | Tool search | Keep definitions out of the initial prompt and retrieve relevant ones when needed. | Discovery adds a lookup step and may add latency; selection behavior needs to be reliable. |
| A workflow makes repetitive, small tool calls. | Programmatic tool calling or code execution | Let a script perform multiple calls and process intermediate data outside the model-visible exchange. | Requires an execution environment and a workflow that can be expressed safely in code; it is not suitable for every tool sequence. |
| Earlier tool results no longer matter. | Context editing | Remove old result blocks once they have served their purpose. | The runtime must decide when information is stale or disposable without losing facts needed later. |
| Stable definitions recur across requests. | Prompt caching | Reduce repeated-input cost for cached content. | Does not reduce the number of tokens in context. |
| Work is independent, bounded, and benefits from parallelism or a separate model-visible conversation. | Subagent | Delegate a defined task and receive a concise result for the coordinator to use. | Orchestration, task framing, and result merging add work; delegation alone does not guarantee token, latency, or cost savings. |
Anthropic’s tool-context guidance suggests tool search when a toolset grows past roughly 20 tools or baseline context becomes noticeable, context editing for conversations long enough that earlier results become irrelevant, and programmatic calling for repetitive small-call chains. These are starting points in Anthropic’s documentation, not universal thresholds. The same guidance recommends prompt caching for stable definitions in its high-volume starting point. See Anthropic’s tool-context guidance for the product-specific details.
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Use subagents for independent work, not as a default cleanup tool
A subagent is a good fit when its work can proceed independently and the coordinator can specify a question and expected result. OpenAI’s multi-agent guide recommends delegating independent tasks with clear outcomes and keeping short or dependent steps in the main agent. A second agent is not a substitute for trimming a bloated tool catalog or managing a long sequence of obsolete results.
Write a delegation contract
Before launching a worker, define the boundary in its task:
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- Question or work product: State the single independent task to complete.
- Role: Describe the expertise or perspective the worker should apply.
- Tool and data access: Specify the capabilities and sources it may use, and exclude those it does not need.
- Completion criteria: Say what evidence or conditions make the task complete.
- Return format: Ask for the concise findings, decisions, or artifacts the coordinator needs.
- Merge plan: Decide how the coordinator will incorporate the result and resolve conflicts with other workers.
This makes the subagent’s tool scope part of the task design rather than a copy of the coordinator’s entire toolbox. For parallel workers editing shared files, explicitly coordinate ownership and changes. OpenAI’s managed Agents API documentation notes that a configured environment filesystem is shared by the coordinator and subagents; separate model context does not mean separate files.
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“Own context” can mean a separate model-visible conversation, not a private copy of everything the runtime uses. OpenAI’s Agents SDK distinguishes local context available to application code from model-visible context. Its documentation says derived wrappers in a run share underlying application context, approval state, and usage tracking; nested Agent.as_tool() runs do not automatically receive isolated copies of application state.
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Tool visibility is also not authorization. A visibility filter can limit which capabilities the SDK exposes, but it does not validate model-generated arguments or resource choices. Enforce decisions in the tool implementation, and use guardrails or approvals where appropriate. MCP servers must authorize their own protected operations. A separate subagent should therefore receive only the tools it needs, while every sensitive operation still enforces access at the appropriate implementation or server boundary. See the Agents SDK context documentation.
Choose the runtime boundary deliberately
Whether you use subagents and tools depends partly on who should own orchestration and execution. OpenAI describes the Agents API as a managed harness for long-running tasks, the Agents SDK as an application-controlled agent loop with tools and handoffs, and the Responses API as a direct integration route. These choices change control over orchestration, storage, tool execution, and the execution environment; they are not interchangeable context-clearing features. Consult the Agents guide for the distinctions and current product details.
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Interpret reported savings narrowly
Anthropic’s engineering article illustrates tool discovery through a filesystem and code-mediated MCP calls, reporting a reduction from 150,000 tokens to 2,000 tokens for that example. That arithmetic is a 98.7% reduction in token count. The article also describes the figure as a time and cost saving, but the example is vendor-reported and does not establish a general result for other workloads or prove that subagent delegation alone produced the savings. Treat it as an illustration of a particular execution pattern, not a benchmark for your runtime. Read Anthropic’s MCP code-execution example for its context.
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A practical decision sequence
- Measure which tokens are growing: definitions, returned results, or conversation history.
- If definitions dominate, test on-demand tool discovery and measure lookup overhead against the reduced baseline prompt.
- If repeated small calls dominate, test code-mediated execution and check that intermediate data can be processed without routing every result through the model.
- If old results dominate, add a context-editing policy that removes them only after their useful information is no longer needed.
- If the remaining task is independent and bounded, delegate it with a narrow tool scope, completion criteria, and a concise return contract.
- Verify separately what is shared: model-visible messages, local application state, approvals, usage tracking, files, and server-side permissions.
Compare these changes using measurements from your own workload. Tool lookup latency, orchestration overhead, and the cost of summarizing and merging results depend on the implementation; the vendor guidance does not establish universal savings for them.
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