documentdb

Plugin documentdb from marketplace azure-documentdb in Azure/documentdb-agent-kit. Commands read from the repo's manifest — today.

Skills (17)

• documentdb-azure-deployment

  Deploy an Azure DocumentDB cluster (`Microsoft.DocumentDB/mongoClusters`) end-to-end — Bicep (primary), Azure CLI one-shot, Terraform, or portal. Covers resource-group creation, cluster parameters (tier, storage, server version, sharding, HA), firewall rule configuration, retrieving the connection s

• documentdb-connection

  Optimize MongoDB client connection configuration (pools, timeouts, patterns) for Azure DocumentDB. Use this skill when working on functions that instantiate or configure a MongoDB client (e.g., calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeo

• documentdb-data-modeling

  Data modeling patterns for Azure DocumentDB — embed vs reference, 16 MB document limit, denormalization for read-heavy workloads, schema versioning. Use when designing new schemas, reviewing existing data models, migrating from SQL, deciding between embedding and referencing, modeling one-to-one / o

• documentdb-driver

  MongoDB driver and SDK best practices for Azure DocumentDB — singleton `MongoClient`, connection reuse, connection-pool fundamentals. Use when writing code that instantiates a MongoDB client, reviewing driver initialization, or diagnosing connection-related bugs. For full connection-pool tuning (ser

• documentdb-full-text-search

  Full-text search best practices for Azure DocumentDB using the `createSearchIndexes` command and `$search` aggregation stage — BM25 keyword scoring, fuzzy search (`maxEdits`), phrase search with `slop`, custom analyzers (keyword + lowerCase + asciiFolding + edgeGram) for prefix / ID matching, `pathH

• documentdb-high-availability

  High availability, business-continuity, and disaster-recovery best practices for Azure DocumentDB — enabling in-region HA with availability zones (99.99% SLA), adding active-passive cross-region replica clusters (99.995% SLA), and understanding automatic backup retention. Use when designing producti

• documentdb-indexing

  Index-type selection and shape guidance for Azure DocumentDB — when to use single-field, compound (ESR), multikey, wildcard, hashed, 2dsphere, TTL, and vector indexes; query-pattern → index-shape cookbook; per-collection index budget; DocumentDB-specific preference for `textSearch` over community `$

• documentdb-local-deployment

  Best practices for running Azure DocumentDB locally for development — choosing the Gateway Docker image (MongoDB on 10260, PostgreSQL on 9712), docker-compose setup, connection config (port 10260, TLS, SCRAM-SHA-256), env-driven configuration, sample-data management (`SKIP_INIT_DATA` / `INIT_DATA_PA

• documentdb-mcp-setup

  Guide users through installing and configuring the DocumentDB MCP server for Azure DocumentDB. Use this skill when a user wants to wire the DocumentDB MCP server into an agentic client (Claude Code, Claude Desktop, Cursor, Copilot CLI, Gemini CLI, VS Code) and define a `CONNECTION_PROFILES` entry, o

• documentdb-monitoring

  Monitoring and diagnostics best practices for Azure DocumentDB — enabling diagnostic settings and slow-query logs, analyzing them in Log Analytics, and alerting on CPU / memory / IOPS / storage / connection saturation per cluster tier. Use when setting up observability on a new cluster, investigatin

• documentdb-natural-language-querying

  Generate read-only DocumentDB/MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate queries for Azure DocumentDB, wants to filter/query/aggregate data, asks "how

• documentdb-query-optimization

  Query and aggregation-pipeline optimization rules for Azure DocumentDB — using `explain("executionStats")` to verify index usage and avoid `COLLSCAN`. Use when reviewing a specific query, diagnosing a slow query, or validating that an index is actually being used. For full index-design workflow, see

• documentdb-query-optimizer

  Help with DocumentDB/MongoDB query optimization and indexing for Azure DocumentDB. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", etc. Do not invoke for general query wri

• documentdb-security

  Security best practices for Azure DocumentDB — TLS enforcement, Private Endpoint / firewall configuration, Microsoft Entra ID + RBAC for authentication, and customer-managed keys (CMK) for encryption at rest. Use when reviewing production security posture, configuring networking, setting up authenti

• documentdb-sharding

  Horizontal sharding (partitioning) for Azure DocumentDB collections — when to shard vs stay single-shard, how to pick a shard key for read-heavy vs write-heavy workloads, the logical/physical shard mental model, scaling out vs scaling up, hot-partition diagnosis, and the `sh.shardCollection` / `sh.r

• documentdb-storage

  Storage configuration guidance for Azure DocumentDB — when and how to use Premium SSD v2 high-performance storage, IOPS/bandwidth caps that are gated by compute tier (not disk size), Premium SSD v2 limitations (no CMK, migration paths, disk-hydration sequencing), and storage capacity change limits.

• documentdb-vector-search

  Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch` / `maxDegree`, Product Quantization (up to 16,000 dims), half-precision (fp16) indexing, and normalizing embeddings for cosine similarity. Use

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