token-optimization
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
What this skill does
# 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.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.