token-optimization

Q: How do I install token-optimization?

Run npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill token-optimization in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/token-optimization/ (personal) or .claude/skills/token-optimization/ (project).

by claude-dev-suiteBackend & APIs

Token optimization best practices for MCP server and tool interactions. Minimizes token consumption while maintaining effectiveness. USE WHEN: user mentions "token usage", "optimize tokens", "reduce API calls", "MCP efficiency", asks about "how to use less tokens", "MCP best practices", "limit output size", "efficient queries" DO NOT USE FOR: Code optimization - use performance instead, Text compression - this is about API usage patterns, Cost optimization (infrastructure) - use cloud/DevOps skills

Installs: 342
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill token-optimization

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads token-optimization automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

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 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.

FAQ

What does the token-optimization skill do?

How do I install token-optimization?

Run npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill token-optimization in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/token-optimization/ (personal) or .claude/skills/token-optimization/ (project).

Which AI agents can use token-optimization?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is token-optimization safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains token-optimization?

It is published by claude-dev-suite in claude-dev-suite/claude-dev-suite. It has 342 recorded installs.