token-optimization

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.