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Found 7 Skills
Guide users through configuring the DocumentDB MCP server for Azure DocumentDB. Use this skill when a user has the DocumentDB MCP server installed but hasn't configured the required environment variables, or when they ask about connecting to Azure DocumentDB and don't have the credentials set up.
Best practices for running Azure DocumentDB locally for development — choosing between the Gateway Docker image and the psql-only image, docker-compose setup, connection config (port 10260, TLS, SCRAM-SHA-256), env-driven configuration, sample-data management (`SKIP_INIT_DATA` / `INIT_DATA_PATH`), port bindings, and dev/prod parity via versioned seed and schema scripts. Use when setting up a new local dev environment, writing sample apps, building integration tests, or diagnosing local connection problems.
High availability and disaster-recovery best practices for Azure DocumentDB — enabling in-region HA (99.99% SLA) and adding active-passive cross-region replica clusters (99.995% SLA). Use when designing production topology, planning failover, provisioning DR, or reviewing cluster architecture.
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 authentication / authorization, or preparing for compliance audits.
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, timeouts, pool exhaustion), optimizing connection-related performance issues. Includes scenarios like building serverless functions, creating API endpoints, optimizing high-traffic applications, or debugging connection failures.
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 when building RAG / semantic-search applications, creating a vector index, tuning recall/latency, or reducing vector-index memory footprint.
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 the `documentdb-query-optimizer` skill.