smithery/vuralserhat86

mongodb_usage

This skill should be used when user asks to "query MongoDB", "show database collections", "get collection schema", "list MongoDB databases", "search records in MongoDB", or "check database indexes".

Installation

$ npx skills add smithery/vuralserhat86 --skill mongodb-usage

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Skill metadata

Parsed from SKILL.md frontmatter.

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skillport
{"category":"auto-healed","tags":["aggregation","big data","cleaning","csv","data analysis","data engineering","data science","database","documents","etl pipelines","export","import","json","machine learning basics","migration","mongodb usage","mongoose","nosql","numpy","pandas","python data stack","query optimization","reporting","schema design","sharding","sql","statistics","transformation","visualization"]}

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,005 B
  • docs SUMMARY.md 219 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

MongoDB MCP Usage

Use the MongoDB MCP server to integrate database queries into workflows.

Read-Only Access

MongoDB MCP is configured in read-only mode. Only queries and data retrieval are supported. No write, update, or delete operations.

Database Queries

Use mcpmongodb* tools for:

  • Listing databases
  • Viewing collection schemas
  • Querying collection data
  • Analyzing indexes

Integration Pattern

  1. List available databases with mcpmongodblist_databases
  2. Explore collections with mcpmongodblist_collections
  3. Get schema information with mcpmongodbgetcollectionschema
  4. Query data as needed for analysis
  5. Format results for user consumption

Environment Variables

MongoDB MCP requires:

  • MONGODB_URI - Connection string (mongodb://...)

Configure in shell before using the plugin.

Cost Considerations

  • Minimize database calls when possible
  • Use schema queries before running analysis queries
  • Cache results locally if multiple calls needed

MongoDB Usage v1.1 - Enhanced

🔄 Workflow

Kaynak: MongoDB Performance Best Practices

Aşama 1: Discovery & Inspection

  • Connection: mcpmongodblist_databases ile erişimi doğrula.
  • Schema Analysis: mcpmongodbgetcollectionschema ile veri tiplerini ve yapıyı anla.
  • Index Check: Mevcut indeksleri listele (list_indexes veya benzeri sorgu ile).

Aşama 2: Query Construction

  • Filter: Sorguları indeksli alanlar (Prefix) üzerinden filtrele.
  • Projection: Sadece gerekli alanları ({ field: 1 }) seç (Network ve RAM tasarrufu).
  • Aggregation: Karmaşık analizler için $match, $group, $project pipeline'ını kur.

Aşama 3: Performance Check (Explain Plan)

  • Explain: Sorgunun COLLSCAN (Tam tarama) mı IXSCAN (Index tarama) mı yaptığını kontrol et.
  • Optimization: Yavaş sorgular için bileşik indeks (Compound Index) öner.

Kontrol Noktaları

Aşama Doğrulama
1 Sorgu 100ms'in altında cevap veriyor mu?
2 "In-memory sort" limiti aşılıyor mu (disk kullanımı var mı)?
3 Regex sorguları indeksin başlangıcını (anchor ^...) kullanıyor mu?