The Tool Desk
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What “enough context” means
Context is the information available to the model while it responds: instructions, the user’s request, relevant conversation history, documents or other references, retrieved material, and tool results. It is not just the text in the initial prompt. Sufficiency is task-relative: an agent needs the facts and capabilities required to meet the task’s success conditions, not a particular number of tokens.
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Also distinguish information the model can use from state held by the surrounding application. For example, the OpenAI Agents SDK documentation explains that local context passed to tools and callbacks is separate from what the language model sees. An application may have a value in memory without exposing it to the model or making it available through a tool. See the OpenAI Agents SDK context documentation.
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1. Define success before reviewing the prompt
Write down the task goal, required facts, constraints, acceptable output, and observable conditions for completion. For an agent workflow, include relevant behaviors such as choosing an appropriate tool, using correct arguments, handing work off when needed, and following instructions. Without explicit criteria, “enough context” risks becoming a subjective impression or a synonym for a large context window.
2. Audit what the model can actually see
At each important decision point, list the instructions, user input, relevant conversation history, documents or workspace references, retrieved content, and tool outputs available to the model. Check the actual model input or execution trace rather than assuming that everything in the application is visible. If essential information is absent, make it available through the instructions, conversation, a suitable tool, or retrieval.
When information is retrieved on demand, include that path in the audit: can the agent discover the source, call the right tool, and receive a useful result? An agent may not need every fact in its initial prompt if it can reliably retrieve the facts it needs.
3. Map each success condition to evidence or capability
For every requirement, identify the information or action needed to meet it. Confirm that the model either has the relevant evidence or can obtain it through an available tool. This exposes missing facts, inaccessible state, and unsupported assumptions. It also helps distinguish a context problem from a tool or workflow problem.
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Keep the context focused. Microsoft’s Visual Studio Code agent-context guidance puts it simply: “Add only the sources that help the agent complete the current task.” The Visual Studio Code documentation on agent tools and context describes this guidance.
4. Inspect the full execution trace
Review a representative run from the request through the final response. Check whether the agent selected appropriate tools, used accurate arguments, handled handoffs appropriately, followed instructions, and made proper use of returned information. Then assess whether its answer is grounded in available evidence and whether the task’s completion conditions were actually met. A polished final answer can hide an avoidable tool failure or an unsupported route to the result.
5. Repeat the evaluation across cases
One successful run is not enough to show that a context setup works reliably. Build a dataset of representative tasks and run the same evaluation criteria across them. Keep the task definitions and scoring stable when comparing prompts, retrieval, routing, tools, or other context changes. Record failure modes alongside aggregate outcomes so that a change that helps one case but harms another is visible.
6. Manage context size as a constraint, not a success metric
Check the applicable model’s context window and token usage when they matter, but do not treat a larger limit as proof of better task performance. Long histories, duplicated tool results, and noisy retrieval can take up space and distract the model. Trimming and compression can help control this overhead. OpenAI’s cookbook article by Emre Okcular, published September 9, 2025, warns: “If too much is carried forward, the model risks distraction, inefficiency, or outright failure.” See the OpenAI cookbook article on session memory.
What to compare between context setups
When comparing two agents or configurations, hold the task definitions and evaluation criteria steady where possible. There is no universal weighting formula; choose weights that fit the task. Useful dimensions include:
- Task completion: Did the agent meet the task’s explicit requirements?
- Instruction adherence: Did it respect instructions and relevant constraints?
- Tool use: Did it choose appropriate tools, make accurate calls, and handle handoffs appropriately?
- Use of tool results: Did it incorporate returned information correctly rather than ignore or misread it?
- Groundedness: Are its claims supported by information available to it?
- Consistency across cases: Does performance hold across representative tasks, and what failure modes recur?
Trace graders and model-based evaluators can help measure these dimensions, but they do not automatically establish correctness. Tie grading to task-specific criteria and review representative failures. OpenAI’s agent evaluation guidance and evaluation guidance discuss evaluation runs and graders.
How to interpret the result
If the agent fails because it lacks a required fact, make that fact visible or provide a reliable retrieval path, then rerun the evaluation. If it cannot access application state, expose the needed information or a suitable tool. If it has the evidence but selects the wrong tool, misuses a result, or violates an instruction, the problem may be tool design, routing, instructions, or execution—not simply insufficient context.
Do not infer a general context-size threshold from a model’s capacity specification. OpenAI’s September 2025 cookbook discusses GPT-5 capacity of up to 272k input tokens and 128k output tokens in a model-specific context. Those figures describe capacity, not whether a particular agent has the right information for a task; model limits and product interfaces can change. The cookbook’s GPT-5 parameters and tools article provides that model-specific discussion.
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