The Tool Desk
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The safest way to reduce Claude Code token usage is not to delete context indiscriminately. Keep stable instructions short, retrieve only the tool data needed for the current decision, and preserve long-running work in a compact state note. This reduces repetition while keeping the facts Claude needs to act correctly.
Start with the right goal: less waste, not less context
Claude Code needs context to understand a repository, follow project rules, remember decisions and verify its work. Removing that information can lower an input count while making the task take more turns, produce more corrections or fail altogether. The practical target is useful context density: a larger share of the context should help answer the question in front of Claude.
Separate context into three categories before changing your workflow:
- Always-needed context: stable project conventions, safety constraints, architecture facts and commands that apply to nearly every task.
- Task context: the files, requirements, errors and decisions relevant to the current change.
- Historical or speculative context: old transcripts, unrelated tool output, abandoned approaches and details that might matter later but do not matter now.
The first category belongs in concise, automatically loaded guidance. The second should be supplied or retrieved when the task requires it. The third should usually be summarized, archived or omitted. This distinction preserves useful context without paying repeatedly for prose that cannot affect the current decision.
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Keep automatically loaded instructions concise
Anthropic’s memory guidance explains that Claude Code automatically loads memory files into context. In practice, that makes project and personal instruction files a recurring cost in every session that loads them. The guidance does not publish a universal token-saving percentage; keeping these files focused is a workflow inference from automatic loading, not a quantified guarantee.
What belongs in a project instruction file
Include rules that are stable and broadly useful:
- How to install dependencies, run tests and format code.
- Directory conventions that are easy to misunderstand.
- Required compatibility targets, security rules and generated-file policies.
- Definition-of-done checks that apply to most changes.
- Short instructions for where task-specific documentation lives.
Write each rule as an actionable statement. “Run the unit suite before reporting success” is more useful than several paragraphs explaining the history of the test system. Link to a local document by path when the explanation is long, then ask Claude to open that document only when the current task needs it.
Move conditional detail out of the always-loaded layer
API inventories, exhaustive endpoint descriptions, migration histories, design discussions and one-off release notes rarely belong in every session. Store them in focused files such as docs/api.md, docs/migrations/2026-10.md or a task brief. The instruction file can say which file to consult and under what condition.
This arrangement keeps the durable rule visible while making detailed material available on demand. It also prevents a common failure mode: Claude sees a large block of old guidance, treats it as equally important to the current request and spends turns reconciling instructions that no longer apply.
Use a compact structure
A practical instruction file can be organized in this order:
- Scope: what repository or package the instructions cover.
- Non-negotiable constraints: security, compatibility and data-handling rules.
- Routine commands: the shortest reliable test, lint and build commands.
- Task routing: paths to deeper documentation, with conditions for reading it.
- Completion checks: what must be verified before the task is considered done.
Review the file when the project changes. Delete obsolete rules rather than appending corrections indefinitely; contradictory guidance consumes context and makes correct behavior less predictable.
Ask tools for narrow results instead of broad dumps
Large tool responses compete for the same context window as your request, repository excerpts and Claude’s own reasoning. Anthropic’s MCP documentation warns about large outputs. It does not establish a single current cutoff that can be treated as a token-saving rule, so optimize for relevance rather than a guessed number.
Specify the smallest useful query
When calling an MCP server, database connector or internal search tool, state the exact question and the fields needed to answer it. Prefer:
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- One record or a bounded date range instead of an entire collection.
- Named fields instead of every column or metadata property.
- Matching lines or a short excerpt instead of a whole document.
- Aggregates, counts or grouped results when individual rows are unnecessary.
- Pagination or a top-N limit when you need to inspect examples.
For a code search, ask for file paths and matching snippets first. Open the few relevant files afterward. For logs, request the error window around a timestamp, not the entire service history. For an issue tracker, retrieve status, title, acceptance criteria and the latest relevant comments rather than every comment ever posted.
Use a two-stage retrieval pattern
- Run a cheap discovery query that returns identifiers, paths or summaries.
- Choose the small set that can change the decision.
- Fetch full content only for those items.
- Stop retrieving once the question is answered and verification is possible.
This pattern retains the evidence needed for a sound decision while avoiding repeated transmission of irrelevant records. It also makes mistakes easier to spot: you can see which items were selected before asking Claude to process their full contents.
Filter at the source when possible
Client-side filtering is weaker than asking the tool to filter. If a connector returns 10,000 rows and Claude must discard 9,900 of them, the context cost has already occurred. Put predicates, field selections, sort order and limits in the tool request. If the tool cannot filter, use a small intermediate script or query outside the conversation and pass only the resulting excerpt.
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Preserve long tasks in a concise external state note
Long chats accumulate exploration, failed approaches and repeated explanations. For a multi-session task, maintain a short note outside the transcript. Anthropic’s prompting guidance discusses context awareness, compaction and saving external state. It does not promise that compaction preserves every detail, so treat the note as the authoritative handoff for decisions you cannot afford to lose.
What the note should contain
- Goal: the user-visible outcome and its boundaries.
- Decisions: choices already made, including rejected alternatives when they prevent rework.
- Completed work: files changed, migrations applied and checks that passed.
- Open issue: the precise blocker, uncertainty or failing test.
- Next action: the smallest concrete step that should happen next.
- Verification: commands, expected results and any environment limitation.
A note might look like this:
Goal: add CSV export for the billing report without changing the JSON API.Decisions: stream rows from the existing query; keep column order from the report schema.
Completed: exporter implemented in src/reports/csv.ts; unit tests pass.
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At the start of a new session, provide the note and the specific next action. Do not paste the entire old transcript unless a disputed detail cannot be recovered from the note or the relevant file.
Tell Claude how to behave when context is compacted
Anthropic’s official Prompting best practices page states: “If you are using Claude in an agent harness that compacts context or allows saving context to external files (like in Claude Code), consider adding this information to your prompt so Claude can behave accordingly.” Use that idea operationally: ask Claude to update the state note before a major phase change, before ending a session and whenever a decision becomes final.
Do not ask for a transcript summary by default. Ask for a decision-oriented handoff with paths, commands and unresolved questions. A shorter note is easier to load and less likely to preserve obsolete conversation details.
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Token use rises when a request combines unrelated goals or leaves success undefined. Split a broad project into stages with an explicit boundary:
- Inspect: identify the relevant files, constraints and current failure.
- Plan: propose a change and list assumptions that require confirmation.
- Implement: edit only the agreed scope.
- Verify: run the smallest meaningful checks and report failures.
- Record: update the external state note.
For each stage, state what Claude may change, what it must not touch and what result ends the stage. “Fix the authentication system” invites a repository-wide investigation. “Trace the 401 from src/middleware/auth.ts, change only the token-expiry handling, and run the auth tests” supplies a bounded search space.
Give acceptance criteria before implementation
Acceptance criteria prevent repeated clarification turns. Include observable conditions such as:
- The response remains backward compatible with a named API version.
- A specific test reproduces the bug before the fix and passes afterward.
- No generated files are edited manually.
- Error messages retain a required code or field.
When criteria are missing, Claude may inspect more files, propose several designs or rerun broad checks because it cannot tell when the task is complete.
Use one decision at a time for ambiguous work
If architecture or product behavior is unclear, ask Claude to identify the smallest set of alternatives and the evidence needed to choose among them. Resolve that decision, then begin implementation. This is more efficient than asking for a complete solution while leaving several assumptions hidden.
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Use continuation, resume and turn limits deliberately
Claude Code’s CLI provides controls for continuing a recent session, resuming a selected session and limiting non-interactive turns. These controls shape workflow; Anthropic does not state that they inherently reduce the tokens required to solve a task.
Continue when the immediate context is still valuable
Use a continuation flow when the current session contains the exact files, decisions and test output needed for the next step. It avoids rebuilding that context manually. Before continuing, remove irrelevant tool output from the next request and state the one action to perform.
Resume when a durable handoff is better than a long transcript
Resume a prior session when its history contains a decision or investigation you still need, but pair the resume with a concise state note. If the session is very long, the note should identify the relevant files and the point at which the old discussion matters; do not assume every historical detail deserves to be reloaded.
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A maximum-turn setting is useful for scheduled jobs, batch reviews and CI experiments. It prevents an unbounded loop from consuming context while repeatedly attempting the same failing action. A cap is a safety boundary, not a promise of lower total usage: a task that needs more turns may simply stop before completion. Log the stopping reason and continue with a focused follow-up when appropriate.
CLI flag names and behavior can change. Check the current Claude Code CLI help and Anthropic documentation before embedding continuation, resume or turn-limit options in automation.
Choose workflow patterns by context trade-off
| Workflow choice | What you keep | What you avoid | Best use |
|---|---|---|---|
| Automatically loaded guidance | Stable rules that apply broadly | Repeated task-specific explanations | Everyday repository work |
| On-demand documents | Detailed evidence when relevant | Loading all design and history files at startup | Specialized features or migrations |
| Filtered tool output | Records and fields that answer the question | Unbounded logs, rows and metadata | MCP, database and search tasks |
| External state note | Goal, decisions, progress and next action | Full transcripts and abandoned exploration | Work spanning sessions |
| Explicit turn limit | A bounded automation attempt | Runaway retries | CI, batch and scheduled jobs |
These are workflow trade-offs, not a published head-to-head benchmark. Select the pattern that preserves the evidence your task needs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Understand tokens separately from billed cost
A token total and a bill are related but not interchangeable. Anthropic’s pricing documentation describes input usage that may include ordinary input tokens, cache creation and cache reads. It also describes model-specific conditions for long-context billing. Rates, thresholds and model eligibility can change, so do not use an old figure as a planning constant.
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Measure before and after a workflow change
- Record the model, task type and session pattern.
- Capture current usage details from the account or API records.
- Change one practice, such as filtering a tool response or moving history into a state note.
- Repeat comparable tasks rather than comparing unrelated requests.
- Inspect both input categories and task outcomes: completion, retries, failures and human corrections.
No reviewed Anthropic material establishes a universal percentage reduction for these practices. Treat your own usage records as the evidence for your environment instead of promising a fixed saving.
Common symptoms and targeted fixes
Claude keeps rereading the same documentation
Put stable navigation rules in the instruction file, but move detailed explanations into named documents. Tell Claude which document answers the current question and ask for the relevant section rather than the whole directory.
Tool calls return huge responses
Add field selection, predicates, date bounds, pagination or an excerpt limit. If the connector lacks those features, create a narrow query or preprocessing step outside the conversation.
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A resumed session repeats old decisions
Create a state note with explicit decisions and the next action. Start the resumed session by treating that note as current, then consult the old transcript only for a named unresolved detail.
An automation loop consumes turns without progress
Set a non-interactive turn limit, require a progress condition after each attempt and record the failure. A follow-up should address the blocker directly rather than rerunning the same broad prompt.
Shorter prompts produce worse changes
The prompt may have removed necessary constraints. Restore the relevant acceptance criteria, file paths, compatibility requirements or error evidence. The objective is not the fewest words; it is the smallest complete context.
A practical operating checklist
- Keep automatically loaded instructions limited to stable, high-value rules.
- Move conditional detail into files that Claude can open on demand.
- Ask every tool for the smallest record set, field set and excerpt that answers the question.
- Use discovery first and full retrieval second.
- Define the task boundary, allowed files and acceptance checks before implementation.
- Maintain a state note with goal, decisions, completed work, open issue and next action.
- Use continuation or resume when the existing context is useful; use a turn cap to bound automation, not to claim savings.
- Inspect current usage records and current pricing documentation before drawing cost conclusions.
The durable principle is simple: preserve decisions and evidence, discard repetition and irrelevant volume. That lets Claude Code retain the context needed for correct work while avoiding the automatic loading, broad retrieval and transcript carry-over that add tokens without adding useful information.
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