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Use cases/Database tools

Database MCP servers

Query and analyze databases in natural language—for analytics and ops (use in controlled environments).

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What is Database tools?

Database MCPs let AI query data in natural language: translate 'daily order volume for the last 30 days' into SQL, explain slow query plans, export reports, and answer schema questions. Postgres / MySQL / SQLite servers handle connections and read-only execution; Redis servers cover key-space inspection.

Security first: connect with a read-only role, narrow visibility via views or row-level security, allow-list or human-confirm writes, and rehearse against staging before production. SQLite / DuckDB MCPs read local files directly—ideal for personal analysis with no connection-string exposure.

Efficiency tip: expose schema-lookup tools so the model fetches relevant table definitions before writing SQL, which largely kills hallucinated columns; codify common queries as shared Skills for consistent analytics.

Good for

  • Dev data exploration
  • Read-only analytics SQL
  • Schema documentation
  • Redis key inspection

Not ideal for

  • Unrestricted writes on production
  • Exposing raw PII to AI
Common stack: Postgres MCP + SQL skill → conversational BI

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SQL

v1.0.4

SkillClawHub5.2k

Writes, reviews, and optimizes SQL queries; designs schemas, indexes, and constraints; plans migrations for any relational database. Use when a query is slow, EXPLAIN shows a sequential scan, or an index is ignored; when rows come back duplicated, missing, or with inflated totals after a JOIN; on deadlocks, lock timeouts, "too many connections", or transactions that never commit; when designing tables, keys, and column types, normalizing or denormalizing a model, or deciding between a JSON column and real columns; for ALTER TABLE on a live table, expand-migrate-contract rollouts, backups and restores, replication lag, connection pooling, partitioning, bulk CSV imports, and moving data between engines; for window functions, CTEs, keyset pagination, upserts, full-text search, multi-tenancy, row-level security, and timezone handling in MySQL, SQLite, MariaDB, or SQL Server. Not for PostgreSQL server internals such as vacuum tuning and work_mem sizing, and not for ORM schema modeling inside a framework.

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PostgreSQL

v1.0.3

SkillClawHub4.9k

Tunes, designs, and operates PostgreSQL: slow queries, indexes, schemas, migrations, vacuum, locks, replication, backups. Use when writing Postgres SQL or psql, designing tables and indexes, reading an EXPLAIN plan, running DDL or a migration against a live table, or when a query suddenly got slow, a table keeps growing while rows stay flat, autovacuum cannot keep up, "too many clients already" appears, a replica lags, deadlocks and lock waits pile up, pg_wal fills the disk, or an upgrade, a restore, or a partitioning plan is on the table. Covers connection pooling and PgBouncer, full-text, trigram and pgvector search, JSONB, roles and row-level security, PITR, extensions, and managed Postgres (RDS, Aurora, Cloud SQL, Neon, Supabase). Not for cross-engine SQL portability or ORM-level modeling.

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MongoDB

v1.0.4

SkillClawHub4.1k

Designs MongoDB schemas, indexes, and aggregation pipelines, and debugs slow queries, connection errors, and replica set failures. Use when modeling documents, deciding embed vs reference, reading an explain plan, or fixing a COLLSCAN, and when a query times out, a pipeline aborts at the memory limit, a cursor dies mid-loop, the pool exhausts and server selection times out, writes fail with duplicate key or "not writable primary", a secondary lags, the oplog window closes, a shard key hotspots, or WiredTiger cache stalls the cluster. Covers mongosh and Compass, Atlas, Mongoose and driver connection strings, transactions and retry loops, change streams, time-series collections, Atlas Search and vector search, sharding, backups, restores, and upgrades. Not for SQL or relational modeling — normalization instincts actively mislead here.

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Redis

v1.0.1

SkillClawHub3.8k

Designs, tunes, and debugs Redis: data structures, memory limits, persistence, Streams and queues, locks, replication, and cluster. Use when writing Redis commands or Lua, choosing between a hash, a sorted set and a stream, setting expirations on cache keys, building a queue, distributed lock, rate limiter, leaderboard, session store or counter, or when Redis answers OOM, MISCONF, CROSSSLOT, MOVED, BUSY, LOADING or WRONGTYPE, latency spikes, memory keeps growing, keys vanish early or never expire, a replica lags or a failover loses writes, KEYS or a big DEL freezes the server, connections are refused, or a cluster reshard, a Valkey / ElastiCache / MemoryDB / Upstash move, or a persistence and backup plan is on the table. Covers redis-cli forensics, pipelining, eviction policies, keyspace notifications, ACLs and exposed-instance hardening, and the JSON, Search and TimeSeries modules. Not for store-agnostic cache hierarchy strategy (caching) or picking a rate-limit algorithm (rate-limiting).

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sql

vdevelop

SkillSkillsMP

Generate safe, correct SQL scripts for the Rock RMS database. Handles INSERT, UPDATE, DELETE, SELECT queries, data migrations, and seed scripts that respect Rock's schema conventions (PersonAlias, audit columns, FK constraints, DefinedType/DefinedValue lookups, Guid-based references). Use when the user says "create a sql script", "write sql", "insert data", "seed data", "data migration", "update records", "sql for Rock", "populate data", "add test data", "query Rock database", or any request involving direct SQL against the Rock RMS database. Also use when the user describes data they want to add, modify, or query in Rock — even if they don't explicitly say "SQL" — such as "add 100 attendance records", "create a new campus", "give Ted Decker some financial transactions", or "set up check-in data". If the task involves Rock database records and SQL is the right tool, use this skill.

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FAQ

Could the AI corrupt production data?

A read-only connection makes writes impossible. When writes are needed, generate SQL → human review → controlled execution, with a rollback plan.

Lightest option for local analysis?

SQLite / DuckDB MCPs point at local files—no server, no credentials. Postgres-style servers need a connection string; target staging.

Can I trust generated SQL?

Have the model read schema, explain, and validate with a small LIMIT first. Human-review SQL for money and deduplication logic on key reports.

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