什么是 io.github.bluwork/postgres-scout-mcp?
用 AI 巡检 PostgreSQL 数据库,提供安全防护、监控能力与数据质量分析。
README
Postgres Scout MCP
Scout your PostgreSQL databases with AI - A production-ready Model Context Protocol server with built-in safety features, monitoring, and data quality tools.
What You Get
You ask:
"How healthy is my production database? Any urgent issues?"
Postgres Scout returns:
Overall Health Score: 78/100
Component Breakdown
| Component | Score | Status |
|---|---|---|
| Cache Performance | 94/100 | Healthy |
| Index Efficiency | 82/100 | Good |
| Table Bloat | 61/100 | Needs Attention |
| Connection Usage | 75/100 | Fair |
Issues Found
- HIGH — Table
ordershas 34% bloat (2.1 GB wasted). VACUUM FULL recommended. - MEDIUM — 3 unused indexes on
sessionsconsuming 890 MB. - LOW — Cache hit ratio for
analytics_eventsis 71% (target: >90%).
Recommendations
- Run
VACUUM FULL ordersduring maintenance window - Drop unused indexes:
idx_sessions_legacy,idx_sessions_old_token,idx_sessions_temp - Consider adding
analytics_eventsto shared_buffers or partitioning by date
That's getHealthScore — one of 38 tools covering exploration, diagnostics, optimization, monitoring, data quality, and safe writes.
Quick Start
Claude Code
claude mcp add postgres-scout -- npx -y postgres-scout-mcp postgresql://localhost:5432/mydb
Then ask: "Show me the largest tables and whether they have any bloat issues."
<details> <summary>Claude Desktop</summary>Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"postgres-scout": {
"command": "npx",
"args": ["-y", "postgres-scout-mcp", "postgresql://localhost:5432/mydb"],
"type": "stdio"
}
}
}
Add to your MCP settings:
{
"postgres-scout": {
"command": "npx",
"args": ["-y", "postgres-scout-mcp", "postgresql://localhost:5432/mydb"]
}
}
The server runs in read-only mode by default. For write operations, run a separate instance:
{
"mcpServers": {
"postgres-scout-readonly": {
"command": "npx",
"args": ["-y", "postgres-scout-mcp", "--read-only", "postgresql://localhost:5432/production"],
"type": "stdio"
},
"postgres-scout-readwrite": {
"command": "npx",
"args": ["-y", "postgres-scout-mcp", "--read-write", "postgresql://localhost:5432/development"],
"type": "stdio"
}
}
}
- postgres-scout-readonly: Safe exploration, no risk of data modification
- postgres-scout-readwrite: Write operations when explicitly needed
Tools
Explore — understand your database
listDatabases— databases the user has access togetDatabaseStats— size, cache hit ratio, connection infolistSchemas— all schemas in the current databaselistTables— tables with size and row statisticsdescribeTable— columns, constraints, indexes, and more
Query — run and analyze
executeQuery— run SELECT queries (or writes in read-write mode)explainQuery— EXPLAIN plans for performance analysisoptimizeQuery— optimization recommendations for a specific query
Diagnose — find problems before they find you
getHealthScore— overall health score with component breakdowndetectAnomalies— anomalies in performance, connections, and dataanalyzeTableBloat— bloat analysis for VACUUM planninggetSlowQueries— slow query analysis (requires pg_stat_statements)suggestVacuum— VACUUM recommendations based on dead tuples and bloat
Optimize — make it faster
suggestIndexes— missing index recommendations from query patternssuggestPartitioning— partitioning strategies for large tablesgetIndexUsage— identify unused or underused indexes
Monitor — watch it live
getCurrentActivity— active queries and connectionsanalyzeLocks— lock contention and blocking queriesgetLiveMetrics— real-time metrics over a time windowgetHottestTables— tables with highest activitygetTableMetrics— comprehensive per-table I/O and scan stats
Data Quality — trust your data
findDuplicates— duplicate rows by column combinationfindMissingValues— NULL analysis across columnsfindOrphans— orphaned records with invalid foreign keyscheckConstraintViolations— test constraints before adding themanalyzeTypeConsistency— type inconsistencies in text columns
Relationships — follow the connections
exploreRelationships— multi-hop foreign key traversalanalyzeForeignKeys— foreign key health and performance
Time Series — temporal analysis
findRecent— rows within a time windowanalyzeTimeSeries— window functions and anomaly detectiondetectSeasonality— seasonal pattern detection
Export — get data out
exportTable— CSV, JSON, JSONL, or SQLgenerateInsertStatements— INSERT statements for migration
Write (read-write only) — safe modifications
previewUpdate/previewDelete— see what would change before committingsafeUpdate— UPDATE with dry-run, row limits, empty WHERE protectionsafeDelete— DELETE with dry-run, row limits, empty WHERE protectionsafeInsert— INSERT with validation, batching, ON CONFLICT support
Security
- Read-only by default — write operations must be explicitly enabled
- All queries use parameterized values
- SQL injection prevention with input validation and pattern detection
- Identifier sanitization for table/column names
- Rate limiting on all operations
- Query timeouts to prevent long-running queries
- Response size limits to prevent memory exhaustion
Examples
"What are the largest tables and do they have bloat?"
listTables({ schema: "public" })
analyzeTableBloat({ schema: "public", minSizeMb: 100 })
"Find duplicate emails in the users table."
findDuplicates({ table: "users", columns: ["email"] })
"Which queries are slowest and how can I speed them up?"
getSlowQueries({ minDurationMs: 100, limit: 10 })
suggestIndexes({ schema: "public" })
"Show me what's happening on the database right now."
getCurrentActivity()
getLiveMetrics({ metrics: ["queries", "connections", "cache"], duration: 30000, interval: 1000 })
getHottestTables({ limit: 5, orderBy: "seq_scan" })
"Find orphaned orders that reference deleted customers."
findOrphans({ table: "orders", foreignKey: "customer_id", referenceTable: "customers", referenceColumn: "id" })
Configuration
| Variable | Default | Description |
|---|---|---|
QUERY_TIMEOUT | 30000 | Query timeout in milliseconds |
MAX_RESULT_ROWS | 10000 | Maximum rows returned per query |
ENABLE_RATE_LIMIT | true | Enable rate limiting |
RATE_LIMIT_MAX_REQUESTS | 100 | Requests per window |
RATE_LIMIT_WINDOW_MS | 60000 | Rate limit window (ms) |
PGMAXPOOLSIZE | 10 | Connection pool max size |
PGMINPOOLSIZE | 2 | Connection pool min size |
PGIDLETIMEOUT | 10000 | Idle connection timeout (ms) |
ENABLE_LOGGING | false | Enable file logging |
LOG_DIR | ./logs | Log file directory |
LOG_LEVEL | info | Log verbosity: debug, info, warn, error |
CLI flags: --read-only (default), --read-write, --mode <mode>
Logging
File logging is disabled by default. Set ENABLE_LOGGING=true to enable. Two log files are created in LOG_DIR:
- tool-usage.log — every tool call with timestamp, name, and arguments
- error.log — errors with stack traces
Connection strings are automatically redacted in all output.
Development
git clone https://github.com/bluwork/postgres-scout-mcp.git
cd postgres-scout-mcp
pnpm install
pnpm build
pnpm test
License
Apache-2.0
常见问题
io.github.bluwork/postgres-scout-mcp 是什么?
用 AI 巡检 PostgreSQL 数据库,提供安全防护、监控能力与数据质量分析。
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