
Token Optimization
- 300 installs
- 27 repo stars
- Updated July 17, 2026
- claude-dev-suite/claude-dev-suite
token-optimization is a Claude dev-suite skill that teaches MCP and agent token-efficiency patterns so developers who run Claude Code skills and tool-heavy workflows can cut prompt, context, and tool-call waste without d
About
token-optimization is a best-practices skill from claude-dev-suite/claude-dev-suite for minimizing token consumption in MCP server and tool interactions. It covers efficient query shapes, output size limits, and context discipline while explicitly excluding code performance tuning, text compression, and cloud infrastructure cost work. The skill allows Read, Grep, and Glob tools and points to mcp__documentation__fetch_docs for deeper reference. Developers reach for token-optimization when agent bills spike, MCP round-trips balloon, or skills return verbose tool output. Trigger phrases include token usage, optimize tokens, reduce API calls, and MCP best practices.
- Lowers LLM context and prompt overhead
- Improves agent cost and latency profile
- Preserves instruction fidelity while trimming noise
- Fits Claude dev-suite agent workflows
- Practical patterns for long-running sessions
Token Optimization by the numbers
- 300 all-time installs (skills.sh)
- Ranked #2,296 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 29, 2026 (Skillselion catalog sync)
npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill token-optimizationAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 300 |
|---|---|
| repo stars | ★ 27 |
| Last updated | July 17, 2026 |
| Repository | claude-dev-suite/claude-dev-suite ↗ |
How do you reduce token usage in MCP agent workflows?
Reduce prompt, context, and tool-call token waste in agent workflows, skills, and dev-suite setups without sacrificing answer quality or critical instructions.
Who is it for?
Developers operating Claude Code skills or MCP-heavy agents who need to lower API token spend without stripping critical instructions.
Skip if: Developers optimizing application runtime performance, compressing static text assets, or cutting cloud infra bills should use performance or DevOps skills instead.
When should I use this skill?
The user mentions token usage, optimize tokens, reduce API calls, MCP efficiency, or how to use fewer tokens in agent workflows.
What you get
Documented MCP query patterns, slimmer tool-call habits, and context limits that lower per-session token burn.
- Token-efficiency checklist
- Refined MCP query patterns
By the numbers
- Allowed-tools list: Read, Grep, Glob
Files
Token Optimization Best Practices
Deep Knowledge: Usemcp__documentation__fetch_docswith technology:token-optimizationfor comprehensive documentation.
Guidelines for minimizing token consumption in MCP server and external tool interactions.
When NOT to Use This Skill
This skill focuses on API/tool call optimization. Do NOT use for:
- Runtime performance - Use
performanceskill for speed optimization - Code minification - Use build tools (Vite, Webpack, etc.)
- Database query optimization - Use database-specific skills
- Algorithm efficiency - Use computer science fundamentals
- Prompt engineering - This is about tool usage, not prompt design
General Principles
| Principle | Description |
|---|---|
| Lazy Loading | Load information only when strictly necessary |
| Minimal Output | Request only needed data, use limit and compact parameters |
| Progressive Detail | Start with overview/summary, drill down only if needed |
| Cache First | Check if information is already in context before external calls |
Anti-Patterns
| Anti-Pattern | Why It's Bad | Token-Efficient Solution |
|---|---|---|
| SELECT * | Returns unnecessary columns | Specify exact columns needed |
| No LIMIT clause | Returns entire dataset | Always add LIMIT (e.g., 100) |
| Full schema requests | Returns massive specs | Use compact=true or format="summary" |
| Recursive documentation fetch | Fetches entire doc tree | Use search_docs with specific query |
| Fetching full logs | Returns thousands of lines | Use tail_logs or find_errors with limit |
| Copy-paste documentation | Duplicates content | Summarize and reference, don't quote verbatim |
| No pagination | Returns all results at once | Use offset/limit for large datasets |
| Full API schema exploration | Multi-MB specifications | Get endpoint list first, details on-demand |
Quick Troubleshooting
| Issue | Check | Solution |
|---|---|---|
| Large MCP response | Output size > 2000 tokens | Add limit parameter, use compact format |
| Repeated API calls | Calling same tool multiple times | Cache results in conversation context |
| Slow context buildup | Too many tool calls | Batch related queries, use more specific tools |
| Unnecessary documentation fetch | Info already known | Check skill files first, fetch docs as last resort |
| Full table scan results | Database query returns too much | Add WHERE clause and LIMIT |
| Verbose error logs | Full stack traces repeated | Summarize errors, reference line numbers |
MCP Server Patterns
database-query
-- BAD: Query without limits
SELECT * FROM users
-- GOOD: Query with filters and limits
SELECT id, name, email FROM users WHERE active = true LIMIT 100Tool usage:
execute_query: ALWAYS uselimitparameter (default: 1000)get_schema(compact=true): For DB structure overviewdescribe_table: Before exploratory queriesexplain_query: Before complex queries on large tables
api-explorer
-- BAD: Full schema
get_api_schema(format="full")
-- GOOD: Summary only for overview
get_api_schema(format="summary")
-- GOOD: Path list with limit
list_api_paths(limit=50)
-- GOOD: Single endpoint details
get_api_endpoint_details(path="/users/{id}", method="GET")Tool usage:
get_api_schema(format="summary"): For API overviewlist_api_paths(limit=50): For endpoint listget_api_models(compact=true): For model list without full schemasearch_api(limit=10): For targeted searches
documentation
-- BAD: Entire document
fetch_docs(topic="react")
-- GOOD: Targeted search
search_docs(query="useEffect cleanup", maxResults=3)Tool usage:
search_docs(maxResults=3): For specific information searchfetch_docs: Only for very specific topics- Check skill files FIRST before fetching documentation
log-analyzer
-- BAD: All logs
parse_logs(file="/var/log/app.log")
-- GOOD: Recent errors only
find_errors(file="/var/log/app.log", limit=50)
-- GOOD: Tail for live debugging
tail_logs(file="/var/log/app.log", lines=50)Tool usage:
tail_logs(lines=50): For recent logsfind_errors(limit=50): For error debuggingparse_logs(limit=200): Only if full analysis needed
security-scanner
Tool usage:
scan_dependencies: Prefer overscan_allscan_secrets: Faster than full scanscan_all: Only for complete audits
code-quality
Tool usage:
analyze_complexity(path="src/specific/file.ts"): Target specific filesfind_duplicates(minLines=10): Filter significant duplicates onlycode_metrics: Compact output for overview
Pre-Call MCP Checklist
Before calling an MCP tool, verify:
- [ ] Do I already have this information in context?
- [ ] Can I use a more specific tool instead of a generic one?
- [ ] Have I set an appropriate
limit? - [ ] Have I used
compact=trueif available? - [ ] Is the expected output reasonable (< 2000 tokens)?
Output Format Standards
For code analysis
- Max 5 issues per category
- Snippets max 10 lines
- Use tables for lists
For database queries
- Max 20 rows in direct output
- For results > 20: "Found N rows. First 20: ..."
- Compact tabular format
For documentation
- Quote only relevant parts (max 500 characters)
- Link to complete docs instead of copying content
- Summarize instead of quoting verbatim
Efficient Response Examples
Database Query - Compact Output
Found 1523 rows. First 20:
| id | name | status |
|----|------|--------|
| 1 | ... | active |
...
Use offset=20 for next page.API Exploration - Progressive Detail
API has 45 endpoints. Summary by tag:
- users: 8 endpoints
- auth: 5 endpoints
- products: 12 endpoints
...
Use get_api_endpoint_details for specifics.Log Analysis - Focused Output
Found 234 errors in last hour. Top 5 by frequency:
1. ConnectionTimeout: 89 occurrences
2. ValidationError: 45 occurrences
...
Use tail_logs or parse_logs with filters for details.Reference Documentation
Deep Knowledge: Usemcp__documentation__fetch_docswith technology:token-optimizationfor advanced optimization techniques.
Related skills
How it compares
Use token-optimization for MCP and agent API token discipline; reach for performance skills when the bottleneck is application code execution speed.
FAQ
What problems does token-optimization address?
token-optimization addresses prompt bloat, oversized MCP tool responses, and inefficient query patterns in Claude agent workflows. The skill targets API token consumption during skill and MCP usage, not application code speed or cloud hosting costs.
Which tools can token-optimization use?
token-optimization is configured with allowed-tools Read, Grep, and Glob so the agent can inspect local skills and repositories while applying efficiency guidance. It also references mcp__documentation__fetch_docs for deeper MCP documentation lookups.