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token-optimization — Claude Code skill: setup and features

Teaches agents to minimize token usage when calling MCP tools and external APIs. Keeps context clean by enforcing limits and compact responses.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add claude-dev-suite/claude-dev-suite --skill token-optimization -a claude-code
Open on GitHub

What token-optimization does

This skill guides how an agent queries databases, API explorers, documentation servers, and log analyzers. Instead of dumping full schemas or fetching entire files, it instructs the model to use compact modes, pagination, and strict bounds. For example, it mandates adding LIMIT to queries and restricts doc fetches via search_docs to 3 results.

The rules enforce strict output thresholds. Database outputs stop at 20 rows, code samples cannot exceed 10 lines, and documentation quotes cap at 500 characters. It relies on standard tools: Read, Grep, and Glob.

This skill does not optimize runtime algorithms, database indexes, or frontend bundles. It targets tool payloads and conversation context exclusively. Unbounded MCP outputs will quickly blow past context limits.

What it helps with

  • truncate large SQL payloads and schema dumps from MCP tools
  • avoid fetching multi-megabyte API schemas by requesting endpoint summaries
  • filter application log queries to recent errors instead of entire files
  • cap documentation citations at 500 characters

Example prompts for the agent

  • Show me how to reduce token consumption when running database queries through MCP
  • How should I query an API explorer tool without overloading the context window?
  • Give me the MCP guidelines for reading server log files efficiently

How to install token-optimization

1Skills CLI (Vercel)

npx skills add claude-dev-suite/claude-dev-suite --skill token-optimization -a claude-code

2Manually: put the files in place

~/.claude/skills/token-optimization/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/token-optimization/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about token-optimization

How do I install token-optimization in Claude Code?

Run npx skills add claude-dev-suite/claude-dev-suite --skill token-optimization -a claude-code. The skill file lands in ~/.claude/skills/token-optimization/SKILL.md.

What does the token-optimization skill do?

It provides interaction rules for external MCP servers to minimize token consumption. The agent learns to request summary views, apply limit parameters, and avoid duplicate API calls.

Can token-optimization improve runtime application speed?

No, it covers model context consumption during tool calls rather than execution speed. For runtime latency, bundling, or database tuning, use dedicated performance tools.

Source text

SKILL.md · MIT
---
name: token-optimization
description: |
  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
allowed-tools: Read, Grep, Glob
---

# 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

```sql
-- 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.

© claude-dev-suite · MIT · claude-dev-suite/claude-dev-suite

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