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48
AI tools
Token / Context Budget Planner
Calculates an approximate sectioned token budget for a chosen context limit and surfaces overflow risk. Model tokenizers, hidden system additions, and provider reserves can change actual usage; values are planning estimates.
✓ Free✓ No account✓ In-browser
QUICK ANSWER
What does this tool do?
Allocate a context window across system instructions, history, sources, user input, output, and safety margin. Token / Context Budget Planner limitation: The tool calls no remote model and neither generates nor verifies model output.
- Input
- For Token / Context Budget Planner, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to allocate a context window across system instructions, history, sources, user input, output, and safety margin.
- Output
- When Token / Context Budget Planner finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin.
- Method
- Token / Context Budget Planner uses this disclosed method to allocate a context window across system instructions, history, sources, user input, output, and safety margin: a rule-based review separates instruction components and calls no remote model.
- Verification
- Before accepting a Token / Context Budget Planner result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin.
TOOL-SPECIFIC RUN PLAN
Go to the workbench ↓See exactly what Token / Context Budget Planner expects and returns
Token / Context Budget Planner uses the contract below to complete “RAG context allocation: local analysis with Token / Context Budget Planner” in particular. Confirm the shape with the example first; use real data only when the fields and expected result are clear.
- Use this shape
1 · Prepare the input
Token / Context Budget Planner — For Token / Context Budget Planner, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to allocate a context window across system instructions, history, sources, user input, output, and safety margin.. Enter the model context limit and expected output allocation. Expected format for Token / Context Budget Planner: For Token / Context Budget Planner, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to allocate a context window across system instructions, history, sources, user input, output, and safety margin..
- Method applied
2 · Run the operation
Token / Context Budget Planner — Token / Context Budget Planner uses this disclosed method to allocate a context window across system instructions, history, sources, user input, output, and safety margin: a rule-based review separates instruction components and calls no remote model. Add estimates for system, history, sources, and user input. Token / Context Budget Planner applies this method: Token / Context Budget Planner uses this disclosed method to allocate a context window across system instructions, history, sources, user input, output, and safety margin: a rule-based review separates instruction components and calls no remote model.
- Expected output
3 · Read the result
Token / Context Budget Planner — When Token / Context Budget Planner finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin.. Long-conversation budgeting: validating the Token / Context Budget Planner output
- Acceptance check
4 · Accept or correct
Token / Context Budget Planner — Before accepting a Token / Context Budget Planner result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin.. Review remaining reserve and verify with the actual tokenizer. Acceptance check for Token / Context Budget Planner: Before accepting a Token / Context Budget Planner result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin..
A tool-specific example path
1. RAG context allocation: local analysis with Token / Context Budget Planner → 2. Long-conversation budgeting: validating the Token / Context Budget Planner output → 3. Output share and safety reserve: checking the limits of Token / Context Budget Planner
Tip: when an example-data button is available, run it first. Do not use the result in a live process unless it passes the acceptance check.
Result
- Limit
- 128,000
- System
- 3,000
- History
- 24,000
- Sources
- 50,000
- User input
- 4,000
- Output allocation
- 8,000
- Safety reserve
- 12,800 (10%)
- Status
- Within budget
- Remaining
- 26,200
CONTEXT BUDGET The actual tokenizer, provider system additions, and tool calls can consume more tokens.
Operation completed
Within budget
Persistent storage
Input and output are not stored. The optional usage counter keeps only tool identity and count, never content.
Verification
Output comes from disclosed rules or browser APIs and needs independent review before high-impact use.
APPLICATION AND DECISION GUIDE
✓ REVIEWEDUse Token / Context Budget Planner with the right input, acceptance check, and next step
Calculates an approximate sectioned token budget for a chosen context limit and surfaces overflow risk. Model tokenizers, hidden system additions, and provider reserves can change actual usage; values are planning estimates. The notes below help you do more than produce a result: they show how to test whether Token / Context Budget Planner fits the task and when to stop before a weak output travels further.
Token / Context Budget Planner uses this disclosed method to allocate a context window across system instructions, history, sources, user input, output, and safety margin: a rule-based review separates instruction components and calls no remote model. The tool uses no remote model or generative LLM. Local explainable heuristics produce suggestions while the user supplies context and the final decision.
For Token / Context Budget Planner, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to allocate a context window across system instructions, history, sources, user input, output, and safety margin. Confirm the shape first with a small example containing no personal data.
When Token / Context Budget Planner finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin. — Before accepting a Token / Context Budget Planner result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin.
Three practical use cases
RAG context allocation: local analysis with Token / Context Budget Planner
Action: Start with a small synthetic fixture that represents this need. Expected input: For Token / Context Budget Planner, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to allocate a context window across system instructions, history, sources, user input, output, and safety margin..
Acceptance signal: The fixture should reproduce “RAG context allocation: local analysis with Token / Context Budget Planner” without real personal data.
Long-conversation budgeting: validating the Token / Context Budget Planner output
Action: Keep that fixture unchanged and run the on-device method: Token / Context Budget Planner uses this disclosed method to allocate a context window across system instructions, history, sources, user input, output, and safety margin: a rule-based review separates instruction components and calls no remote model.
Acceptance signal: Identical input should return the same result, with no network or file action assumed beyond the disclosed method.
Output share and safety reserve: checking the limits of Token / Context Budget Planner
Action: Retain the output record before moving it into the target workflow: When Token / Context Budget Planner finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin..
Acceptance signal: Acceptance requires Before accepting a Token / Context Budget Planner result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin.; otherwise do not move the result forward.
Do not use the result for a decision beyond this boundary: Token / Context Budget Planner limitation: The tool calls no remote model and neither generates nor verifies model output.
Move the result to another tool or live process only after Before accepting a Token / Context Budget Planner result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin.. Keep this limit visible in the decision record: Token / Context Budget Planner limitation: The tool calls no remote model and neither generates nor verifies model output.
A result in three steps
- 01
Enter the model context limit and expected output allocation. Expected format for Token / Context Budget Planner: For Token / Context Budget Planner, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to allocate a context window across system instructions, history, sources, user input, output, and safety margin..
- 02
Add estimates for system, history, sources, and user input. Token / Context Budget Planner applies this method: Token / Context Budget Planner uses this disclosed method to allocate a context window across system instructions, history, sources, user input, output, and safety margin: a rule-based review separates instruction components and calls no remote model.
- 03
Review remaining reserve and verify with the actual tokenizer. Acceptance check for Token / Context Budget Planner: Before accepting a Token / Context Budget Planner result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin..
When is this tool useful?
- ✓ RAG context allocation: local analysis with Token / Context Budget Planner
- ✓ Long-conversation budgeting: validating the Token / Context Budget Planner output
- ✓ Output share and safety reserve: checking the limits of Token / Context Budget Planner
Tool-specific limitation
Token / Context Budget Planner limitation: The tool calls no remote model and neither generates nor verifies model output.
LEARN THE WORKFLOW
Guides for this tool
Browser-Only Agentic AI: Designing Safe Tool Orchestration
Combine semantic search, multi-step plans, visible rationale, error translation, and on-device voice without uploading data.
Read guide →Token and Context Budgets: A Practical System-Prompt Checklist
Turn system instructions, history, sources, user input, output, and safety margin into a measurable context plan.
Read guide →ABOUT THIS TOOL
Frequently asked questions
What input does Token / Context Budget Planner accept?+
For Token / Context Budget Planner, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to allocate a context window across system instructions, history, sources, user input, output, and safety margin. Enter the model context limit and expected output allocation. Expected format for Token / Context Budget Planner: For Token / Context Budget Planner, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to allocate a context window across system instructions, history, sources, user input, output, and safety margin..
What does Token / Context Budget Planner return?+
When Token / Context Budget Planner finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin. Token / Context Budget Planner uses this disclosed method to allocate a context window across system instructions, history, sources, user input, output, and safety margin: a rule-based review separates instruction components and calls no remote model.
How should I validate Token / Context Budget Planner output?+
For “RAG context allocation: local analysis with Token / Context Budget Planner”, first complete “Add estimates for system, history, sources, and user input. Token / Context Budget Planner applies this method: Token / Context Budget Planner uses this disclosed method to allocate a context window across system instructions, history, sources, user input, output, and safety margin: a rule-based review separates instruction components and calls no remote model.”, then apply this check: “Review remaining reserve and verify with the actual tokenizer. Acceptance check for Token / Context Budget Planner: Before accepting a Token / Context Budget Planner result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to allocate a context window across system instructions, history, sources, user input, output, and safety margin..”. Do not use a consequential result before a second test with boundary or malformed input.
Does this tool send or store input on a server?+
No. Processing runs in this browser tab and tool input is not persisted. Copying, downloading, or transferring happens only when you choose it.