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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-optimization

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Listed on Skillselion
Installs300
repo stars27
Last updatedJuly 17, 2026
Repositoryclaude-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

SKILL.mdMarkdownGitHub ↗

Token Optimization Best Practices

Deep Knowledge: Use mcp__documentation__fetch_docs with technology: token-optimization for 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 performance skill 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

PrincipleDescription
Lazy LoadingLoad information only when strictly necessary
Minimal OutputRequest only needed data, use limit and compact parameters
Progressive DetailStart with overview/summary, drill down only if needed
Cache FirstCheck if information is already in context before external calls

Anti-Patterns

Anti-PatternWhy It's BadToken-Efficient Solution
SELECT *Returns unnecessary columnsSpecify exact columns needed
No LIMIT clauseReturns entire datasetAlways add LIMIT (e.g., 100)
Full schema requestsReturns massive specsUse compact=true or format="summary"
Recursive documentation fetchFetches entire doc treeUse search_docs with specific query
Fetching full logsReturns thousands of linesUse tail_logs or find_errors with limit
Copy-paste documentationDuplicates contentSummarize and reference, don't quote verbatim
No paginationReturns all results at onceUse offset/limit for large datasets
Full API schema explorationMulti-MB specificationsGet endpoint list first, details on-demand

Quick Troubleshooting

IssueCheckSolution
Large MCP responseOutput size > 2000 tokensAdd limit parameter, use compact format
Repeated API callsCalling same tool multiple timesCache results in conversation context
Slow context buildupToo many tool callsBatch related queries, use more specific tools
Unnecessary documentation fetchInfo already knownCheck skill files first, fetch docs as last resort
Full table scan resultsDatabase query returns too muchAdd WHERE clause and LIMIT
Verbose error logsFull stack traces repeatedSummarize 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 100

Tool usage:

  • execute_query: ALWAYS use limit parameter (default: 1000)
  • get_schema(compact=true): For DB structure overview
  • describe_table: Before exploratory queries
  • explain_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 overview
  • list_api_paths(limit=50): For endpoint list
  • get_api_models(compact=true): For model list without full schema
  • search_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 search
  • fetch_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 logs
  • find_errors(limit=50): For error debugging
  • parse_logs(limit=200): Only if full analysis needed

security-scanner

Tool usage:

  • scan_dependencies: Prefer over scan_all
  • scan_secrets: Faster than full scan
  • scan_all: Only for complete audits

code-quality

Tool usage:

  • analyze_complexity(path="src/specific/file.ts"): Target specific files
  • find_duplicates(minLines=10): Filter significant duplicates only
  • code_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=true if 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: Use mcp__documentation__fetch_docs with technology: token-optimization for 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.

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