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To keep an AI agent’s context focused, ask tools for the fields, ranges, or pages the next step needs; set workload-specific output limits; and provide a way to retrieve omitted details. Preserve where results came from and treat text inside them as data—not instructions. If hostile content or a hard security boundary is a concern, add screening and technical controls: a natural-language rule alone cannot enforce one.
Start with the source of context pressure
“Tool output” can mean a large result, a long chain of intermediate calls, or even the definitions describing available tools. Those are different problems, so trimming every result is not a universal fix. Anthropic describes several separate context-management techniques, each aimed at a different source of overhead.
| Problem | Approach | How to apply it |
|---|---|---|
| Too many tool definitions in context | Tool search loads definitions on demand. | Choose the tools needed for the task before cutting useful results from a tool already in use. |
| Too many intermediate call-and-result roundtrips | Programmatic tool calling can keep intermediate results out of conversation history. | Consider batching a repeated chain if your implementation supports it. |
| Repeated tool-definition cost | Prompt caching can amortize repeated definitions. | This reduces repeated input cost; it does not reduce how much context those definitions occupy. |
| Old results no longer matter | Context editing removes old tool results. | Clear stale material after it has served its purpose. |
| A single response is too large | Pagination, range selection, filtering, or truncation with sensible defaults. | Prefer targeted retrieval and bounded output, and make omitted details retrievable. |
Anthropic recommends “some combination of pagination, range selection, filtering, and/or truncation with sensible default parameter values” for responses that could consume substantial context (Anthropic’s tool-design guidance). Its examples address tool design; the appropriate limit depends on the tool and workload.
Write a rule that preserves what the next step needs
A useful rule is task-specific: it says what information to retain, what can be omitted, and how to recover missing detail. One practical pattern is:
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- Purpose: State what the next reasoning step must learn or decide.
- Selection: Request matching fields, relevant ranges, or a page of results instead of an unbounded dump where possible.
- Bounds: Set a maximum size or pagination behavior appropriate to this tool and workload. No universal safe token limit is established; choose a limit by measuring representative outputs.
- Retention: Keep the fields needed to support the answer, plus source identity and status or error information that changes how the result should be interpreted.
- Recovery: Specify how to request another range, page, or targeted query when the selected output is insufficient.
- Trust boundary: Mark third-party content as untrusted data. Text found in a result must not silently become a governing instruction.
- Escalation: Add screening and technical controls when injection is suspected or a security guarantee is required.
- Validation: Compare task quality and context use before and after filtering. Test large, malformed, incomplete, and adversarial results.
For example, a rule for a paginated issue search could ask for only the issue identifier, title, status, and matching excerpt; cap each response at a chosen page size; preserve the search source and any error status; and request another page or a targeted issue record if the excerpt is insufficient. The page size should be selected for that integration and workload, not copied as a universal safety value.
Keep untrusted content separate from instructions
A shortened result can still carry hostile instructions. Anthropic’s injection guidance identifies web pages, emails, documents, and tool results as possible carriers of indirect prompt injection. Its recommendation is to keep third-party content in tool-result blocks and make its source and nature explicit. The documentation states: “Put untrusted content only in tool results” (Anthropic’s injection-mitigation guidance).
Trimming and security screening solve related but distinct problems. Filtering reduces irrelevant material; it does not establish that retained material is trustworthy. Anthropic describes screening raw tool output with a classifier and returning an error or stripped summary if injection is suspected. It also advises testing prompt defenses, since added complexity can harm performance on other tasks. Keep provenance when filtering, and test the defense against both hostile inputs and ordinary work.
Know what a token limit does—and does not—tell you
Anthropic’s engineering article gives Claude Code as a product-specific example, describing a default tool-response limit of 25,000 tokens. That figure is a vendor default for that product, not a general safe limit for agents or a guarantee that a response below it is relevant or safe. The article’s publication year is not shown in the search result, so confirm current Claude Code behavior with the product documentation before relying on the figure.
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The sources reviewed establish no universal safe truncation size, benchmark comparing trimming rules across vendors, or measured accuracy improvement from a particular filter. Choose bounds empirically for the integration and task, and preserve a retrieval path rather than assuming the first bounded response is complete.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use technical controls for boundaries that must hold
A prose instruction can guide model behavior, but it is not a hard execution boundary. OpenAI’s account of Codex safety describes technical sandbox boundaries alongside rules, authorization decisions, and telemetry. When an outcome must be enforced—such as restricting what a tool can access—use appropriate technical controls rather than relying on an instruction to the model. OpenAI published that account on May 8, 2026 (OpenAI’s Codex safety overview).
Validate the complete setup against representative routine results and difficult cases: oversized responses, malformed or incomplete data, errors, and content containing instructions aimed at the agent. Check both whether the next step still has the facts it needs and whether the system handles untrusted material and access boundaries as intended.
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