Best MCP Servers for Database Access in 2026

Why Database MCP Servers Matter

Connecting AI to your database is one of the most impactful uses of the Model Context Protocol. Instead of manually copying schema information or writing queries by hand, database MCP servers let AI models directly explore your data structure, run queries, and even help with migrations - all through a secure, standardized interface.

In this guide, we compare the five best database MCP servers available in the MCPgee directory, covering PostgreSQL, SQLite, MongoDB, Redis, and Elasticsearch. We go deep on each one: exact configuration files for Claude Desktop and Cursor, real query examples, performance characteristics, limitations, and the gotchas that documentation rarely mentions. Whether you're building an AI-powered analytics tool or just want Claude to help debug your queries, there's a server here for you.

Quick Comparison Table

Before we dive into each server, here is how they stack up at a glance:

Server	Best For	DB Type	Auth	Install Method	Stars	License
PostgreSQL	Production relational DBs	Relational (SQL)	Connection string / SSL	npx	1.2k+	MIT
SQLite	Local dev & prototyping	Embedded (SQL)	File path only	npx / uvx	1k+	MIT
MongoDB	Document databases	Document (NoSQL)	Connection string / Atlas	npx	500+	Apache 2.0
Redis	Caching & real-time data	Key-Value / Data Structures	redis:// URL or host/port	npx / Docker	400+	MIT
Elasticsearch	Search & log analytics	Search engine (NoSQL)	API key / basic auth	npx / Docker	300+	MIT

How Database MCP Servers Work Under the Hood

Before evaluating individual servers, it helps to understand the protocol flow that every database MCP server follows. This is the same pattern regardless of whether you are querying Postgres or browsing Redis keys.

Step 1 - Tool Discovery. When the AI client (Claude Desktop, Cursor, VS Code Copilot) connects to a database MCP server, it first calls the tools/list endpoint. The server responds with a JSON array of available tools, for example query, list_tables, describe_table, and execute. Each tool includes a JSON Schema describing its parameters.

Step 2 - Schema Introspection. The AI model typically starts by calling list_tables or an equivalent tool. The MCP server translates this into the appropriate database command - SELECT table_name FROM information_schema.tables for Postgres, .tables for SQLite, db.getCollectionNames() for MongoDB - and returns structured JSON back through the protocol.

Step 3 - Query Execution. When the AI decides it needs data, it sends a tools/call request with the tool name (e.g., query) and parameters (e.g., {"sql": "SELECT * FROM users WHERE created_at > '2026-01-01' LIMIT 10"}). The MCP server parses the query, optionally validates it against a read-only policy, executes it against the database, and returns the result set as structured JSON content.

Step 4 - Result Handling. The MCP server serializes rows into JSON objects and wraps them in the standard MCP content response format. The AI client receives this structured data and can reason about it, generate follow-up queries, or present it to the user. Large result sets are typically truncated at the server level - most servers default to returning 100-500 rows maximum.

The key insight is that the MCP server acts as a controlled gateway. The AI never holds a raw database connection. Every query passes through the server's validation layer, which can enforce read-only access, query timeouts, row limits, and schema restrictions.

1. PostgreSQL MCP Server - Best for Production Databases

Overview

The PostgreSQL MCP server is the go-to choice for teams running production Postgres databases. It provides full SQL query support, schema introspection, and connection pooling - making it ideal for letting AI models explore complex relational schemas. Maintained as part of the official @modelcontextprotocol organization, it receives regular updates and has the largest user base of any database MCP server.

Key Features

• Schema introspection: AI can browse tables, columns, indexes, foreign keys, and constraints without manual documentation
• Query execution: Run SELECT queries safely with configurable read-only modes
• Connection pooling: Handles multiple concurrent connections efficiently via pg-pool
• SSL support: Encrypted connections for production environments
• Transaction support: Wraps queries in read-only transactions by default

Claude Desktop Configuration

Add this to your claude_desktop_config.json file (located at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres",
        "postgresql://readonly_user:secure_password@localhost:5432/myapp_production"
      ]
    }
  }
}

For SSL connections to cloud-hosted Postgres (e.g., Supabase, Neon, RDS), append ?sslmode=require to the connection string.

Cursor Configuration

In Cursor, open Settings > MCP and add a new server, or edit .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres",
        "postgresql://readonly_user:secure_password@localhost:5432/myapp_dev"
      ]
    }
  }
}

What AI Can Actually Do With It

Once connected, you can ask your AI assistant things like:

-- "Show me all tables with more than 1 million rows"
SELECT schemaname, relname, n_live_tup
FROM pg_stat_user_tables
WHERE n_live_tup > 1000000
ORDER BY n_live_tup DESC;

-- "Find users who signed up in the last 7 days but haven't made a purchase"
SELECT u.id, u.email, u.created_at
FROM users u
LEFT JOIN orders o ON o.user_id = u.id
WHERE u.created_at > NOW() - INTERVAL '7 days'
AND o.id IS NULL;

-- "What indexes exist on the orders table?"
SELECT indexname, indexdef
FROM pg_indexes
WHERE tablename = 'orders';

Performance Characteristics

Schema introspection calls (list_tables, describe_table) typically return in 20-80ms on databases with under 200 tables. Query execution time depends entirely on the query itself and your Postgres configuration. The MCP server adds roughly 5-15ms of overhead per request for JSON serialization. Connection pooling keeps the first-query latency low after initial startup (which takes 1-3 seconds for npx to download and launch).

Limitations and Gotchas

• The server sends the entire schema as tool descriptions on connect. Databases with 500+ tables can cause slow startup and high token usage during the initial handshake.
• No built-in query timeout - a poorly-constructed AI query can hold a connection indefinitely. Set statement_timeout on your Postgres role: ALTER ROLE readonly_user SET statement_timeout = '10s';
• Binary columns (bytea) are returned as hex strings, which inflates token counts.
• No support for COPY or multi-statement transactions.

Community and Maintenance

Actively maintained by the MCP core team at Anthropic. Issues are typically triaged within 48 hours. The server sees 2-3 releases per month. It is the most battle-tested database MCP server available.

When to Use It

Choose PostgreSQL MCP when you need AI to understand complex relational schemas, help write queries, or analyze production data. It pairs especially well with Grafana MCP for observability workflows.

2. SQLite MCP Server - Best for Local Development

Overview

The SQLite MCP server is perfect for local development, prototyping, and working with embedded databases. It's lightweight, requires no external services, and works out of the box with any SQLite database file. If you want to try database MCP servers for the first time, start here.

Key Features

• Zero configuration: Just point it at a .db file
• Full SQL support: Read and write operations
• Schema browsing: Automatic table and column discovery
• Cross-platform: Works on Windows, macOS, and Linux
• Business analytics tools: Built-in create_chart tool for generating visualizations from query results

Claude Desktop Configuration

{
  "mcpServers": {
    "sqlite": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sqlite",
        "/Users/you/projects/myapp/data/app.db"
      ]
    }
  }
}

Cursor Configuration

{
  "mcpServers": {
    "sqlite": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sqlite",
        "./data/app.db"
      ]
    }
  }
}

What AI Can Actually Do With It

-- "Create a new analytics table and populate it from existing data"
CREATE TABLE daily_signups AS
SELECT DATE(created_at) as signup_date, COUNT(*) as count
FROM users
GROUP BY DATE(created_at);

-- "Show me the schema for this database"
SELECT name, sql FROM sqlite_master
WHERE type = 'table'
ORDER BY name;

-- "Find duplicate email addresses"
SELECT email, COUNT(*) as cnt
FROM users
GROUP BY email
HAVING cnt > 1;

Performance Characteristics

SQLite MCP is extremely fast for small to medium databases. Schema introspection completes in under 5ms. Queries on tables with under 100k rows return in 10-50ms. The server has near-zero startup overhead compared to Postgres MCP because there is no network connection to establish. However, SQLite locks the entire database file during writes, so concurrent write operations from multiple tools can cause SQLITE_BUSY errors.

Limitations and Gotchas

• Write operations are enabled by default. If you only want AI to read data, there is no built-in read-only flag - you need to set file permissions on the .db file or use a read-only connection string (file:app.db?mode=ro).
• The server does not handle WAL mode conflicts well if another process is writing to the same database simultaneously.
• Maximum database size is practically limited to a few GB before performance degrades significantly.
• No support for concurrent connections - the server uses a single connection internally.

Community and Maintenance

Part of the official MCP reference servers. Maintenance is steady but less frequent than Postgres since SQLite has fewer edge cases. Community contributions focus mainly on adding new tool capabilities.

When to Use It

Perfect for getting started with MCP or working with local datasets. If you're building a prototype and don't want to set up a full database server, SQLite MCP is your best bet.

3. MongoDB MCP Server - Best for Document Databases

Overview

The MongoDB MCP server brings NoSQL support to the MCP ecosystem. Maintained by MongoDB Inc. directly, it handles document queries, aggregation pipelines, and collection management - critical for teams using MongoDB as their primary data store.

Key Features

• Document queries: Find, aggregate, and analyze documents with the full MongoDB query language
• Collection management: Browse collections, view document counts, and sample documents
• Aggregation pipelines: AI can help build complex multi-stage aggregation queries
• Atlas support: Works with MongoDB Atlas cloud deployments including free-tier clusters
• Index analysis: View existing indexes and get AI suggestions for new ones

Claude Desktop Configuration

{
  "mcpServers": {
    "mongodb": {
      "command": "npx",
      "args": [
        "-y",
        "mongodb-mcp-server",
        "--connectionString",
        "mongodb+srv://readonly:password@cluster0.abc123.mongodb.net/myapp"
      ]
    }
  }
}

Cursor Configuration

{
  "mcpServers": {
    "mongodb": {
      "command": "npx",
      "args": [
        "-y",
        "mongodb-mcp-server",
        "--connectionString",
        "mongodb://localhost:27017/myapp_dev"
      ]
    }
  }
}

What AI Can Actually Do With It

// "Find all orders over $100 from the last month with customer details"
db.orders.aggregate([
  { $match: {
    total: { $gt: 100 },
    createdAt: { $gte: new Date("2026-04-20") }
  }},
  { $lookup: {
    from: "customers",
    localField: "customerId",
    foreignField: "_id",
    as: "customer"
  }},
  { $unwind: "$customer" },
  { $project: {
    total: 1,
    "customer.name": 1,
    "customer.email": 1,
    createdAt: 1
  }}
])

// "What's the average order value by product category?"
db.orders.aggregate([
  { $unwind: "$items" },
  { $group: {
    _id: "$items.category",
    avgValue: { $avg: "$items.price" },
    totalOrders: { $sum: 1 }
  }},
  { $sort: { avgValue: -1 } }
])

Performance Characteristics

Collection listing completes in 15-40ms for databases with under 100 collections. Document queries depend on your indexes - the MCP server itself adds about 10-20ms of overhead. The main performance concern is with aggregation pipelines on large collections: AI-generated pipelines that lack $match stages early in the pipeline can trigger full collection scans. Atlas connections add 50-150ms of network latency depending on your cluster region.

Limitations and Gotchas

• Schema inference is sampled, not exhaustive. The server samples a handful of documents to infer field types, which may miss rarely-used fields or polymorphic schemas.
• AI-generated aggregation pipelines can be expensive. There is no built-in cost estimation - a poorly-written pipeline can consume significant cluster resources.
• The --readOnly flag restricts write operations but does not prevent expensive read queries.
• Large documents (over 1MB) are truncated in responses, which can confuse the AI about the actual data structure.
• GridFS files and binary fields are not accessible through the MCP server.

Community and Maintenance

Officially maintained by MongoDB Inc. The server is under active development with new tools being added regularly. As an official product, it benefits from MongoDB's QA and security processes. Support is available through MongoDB's community forums.

When to Use It

Choose MongoDB MCP when your data lives in document databases. It's particularly useful for AI-assisted data exploration and building aggregation pipelines, which can be notoriously complex to write manually.

4. Redis MCP Server - Best for Caching & Real-Time Data

Overview

The Redis MCP server connects AI to your caching layer and real-time data stores. It supports key-value operations, pub/sub monitoring, and data structure inspection. Unlike the SQL-based servers, Redis MCP is most useful for debugging and operational tasks rather than analytical queries.

Key Features

• Key-value operations: GET, SET, DEL, and browse keys by pattern
• Data structure support: Lists, sets, hashes, sorted sets, streams
• TTL inspection: Check expiration times on cached data
• Pattern matching: Search keys with glob patterns (e.g., session:*)
• Memory analysis: Check memory usage per key

Claude Desktop Configuration

{
  "mcpServers": {
    "redis": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-redis",
        "redis://localhost:6379"
      ]
    }
  }
}

Cursor Configuration

{
  "mcpServers": {
    "redis": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-redis",
        "redis://:your_password@redis-host:6379/0"
      ]
    }
  }
}

What AI Can Actually Do With It

# "How many active sessions are there right now?"
KEYS session:*
# Returns matching keys; AI counts them and reports

# "What's cached for user 12345?"
HGETALL user:12345:cache
# Returns all fields in the hash

# "Show me keys expiring in the next 60 seconds"
# AI iterates through SCAN and checks TTL on each key
SCAN 0 MATCH cache:* COUNT 100
TTL cache:product:789
# Returns remaining seconds - AI flags keys close to expiration

# "What's the memory footprint of our session data?"
MEMORY USAGE session:abc123
DEBUG OBJECT session:abc123

Performance Characteristics

Redis MCP is the fastest of all database MCP servers. Simple GET/SET operations complete in under 2ms. SCAN operations on databases with millions of keys take longer but are non-blocking. The main bottleneck is pattern-matching with KEYS * on large databases - AI sometimes generates this command, which blocks Redis for seconds on databases with millions of keys. The server should be configured to use SCAN instead, and most implementations do this automatically.

Limitations and Gotchas

• KEYS command danger: If the AI generates a KEYS * command on a production Redis instance with millions of keys, it will block all other operations. Make sure the MCP server implementation uses SCAN internally.
• No built-in read-only mode in most implementations. You need to use Redis ACLs to restrict the connecting user: ACL SETUSER mcp_readonly on >password ~* +get +scan +hgetall +ttl +type +dbsize -set -del -flushall
• Binary values (e.g., serialized PHP sessions, protobuf data) are returned as escaped strings that waste tokens and are unreadable to AI.
• Pub/sub monitoring is not supported in most MCP implementations due to the streaming nature of subscriptions.
• Cluster mode requires connecting to each node separately - the MCP server does not automatically follow redirects.

Community and Maintenance

Part of the official MCP reference servers. The Redis server is simpler than Postgres/SQLite and receives less frequent updates, but the codebase is stable. Community contributions have added support for Redis Streams and sorted set operations.

When to Use It

Ideal when debugging caching issues, monitoring real-time data, or letting AI analyze your Redis data structures. Pairs well with the Datadog MCP server for full-stack observability.

5. Elasticsearch MCP Server - Best for Search & Analytics

Overview

The Elasticsearch MCP server enables AI to query search indexes, analyze logs, and build complex search queries. If you're running the ELK stack, this is essential for letting AI help you write Elasticsearch queries - arguably the most complex query language in common use.

Key Features

• Full-text search: Execute search queries across indexes with all Elasticsearch query DSL features
• Index management: Browse mappings, aliases, and index health
• Aggregations: Run analytics queries on log data - terms, date histograms, percentiles
• Cluster monitoring: Check cluster health, node status, and shard allocation
• Template management: View and manage index templates

Claude Desktop Configuration

{
  "mcpServers": {
    "elasticsearch": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-elasticsearch"
      ],
      "env": {
        "ELASTICSEARCH_URL": "https://localhost:9200",
        "ELASTICSEARCH_API_KEY": "your_base64_api_key_here"
      }
    }
  }
}

Cursor Configuration

{
  "mcpServers": {
    "elasticsearch": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-elasticsearch"
      ],
      "env": {
        "ELASTICSEARCH_URL": "https://localhost:9200",
        "ELASTICSEARCH_USERNAME": "elastic",
        "ELASTICSEARCH_PASSWORD": "your_password"
      }
    }
  }
}

What AI Can Actually Do With It

// "Find all 5xx errors in the last hour from the API gateway"
POST /logs-*/_search
{
  "query": {
    "bool": {
      "must": [
        { "range": { "@timestamp": { "gte": "now-1h" } } },
        { "range": { "response.status": { "gte": 500, "lt": 600 } } },
        { "term": { "service.name": "api-gateway" } }
      ]
    }
  },
  "sort": [{ "@timestamp": "desc" }],
  "size": 50
}

// "What's the P99 response time by endpoint over the last 24 hours?"
POST /metrics-*/_search
{
  "size": 0,
  "query": { "range": { "@timestamp": { "gte": "now-24h" } } },
  "aggs": {
    "by_endpoint": {
      "terms": { "field": "http.route.keyword", "size": 20 },
      "aggs": {
        "p99_latency": {
          "percentiles": { "field": "http.duration_ms", "percents": [99] }
        }
      }
    }
  }
}

Performance Characteristics

Index listing and mapping retrieval complete in 30-100ms. Search queries depend entirely on your cluster and data volume. The MCP server adds 10-25ms of overhead for serialization. The biggest performance concern is AI generating unbounded aggregations - an aggregation with "size": 10000 on a high-cardinality field can overwhelm your cluster. Most Elasticsearch MCP servers default to "size": 10 for aggregation buckets.

Limitations and Gotchas

• Elasticsearch query DSL is complex enough that AI models sometimes generate syntactically invalid queries, especially for nested aggregations. Expect some trial and error.
• The server returns raw Elasticsearch responses, which can be very large. A search returning 50 documents with many fields will consume thousands of tokens.
• Self-signed SSL certificates require setting NODE_TLS_REJECT_UNAUTHORIZED=0 in the env config, which has security implications.
• No built-in support for Kibana saved objects - you cannot reference existing dashboards or visualizations.
• Cross-cluster search is not supported through the MCP server.

Community and Maintenance

Maintained as part of the MCP reference servers. Elasticsearch MCP has a smaller but dedicated community. Updates tend to follow Elasticsearch major version releases. Compatibility is tested against Elasticsearch 7.x and 8.x.

When to Use It

Choose Elasticsearch MCP when working with search applications, log analysis, or any system backed by Elasticsearch. It's particularly powerful for AI-assisted log debugging and search relevance tuning.

Setting Up Multiple Database Servers

In real-world applications, you rarely use just one database. Here's how to configure Claude Desktop or Cursor to connect to Postgres, Redis, and Elasticsearch simultaneously - giving AI a complete view of your stack:

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres",
        "postgresql://readonly:pass@localhost:5432/myapp"
      ]
    },
    "redis": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-redis",
        "redis://localhost:6379"
      ]
    },
    "elasticsearch": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-elasticsearch"
      ],
      "env": {
        "ELASTICSEARCH_URL": "http://localhost:9200"
      }
    }
  }
}

With this setup, you can ask AI questions that span your entire stack: "Check if user 42's session exists in Redis, then find their recent orders in Postgres, and see if there are any error logs related to their account in Elasticsearch." The AI will call tools from each server as needed.

Important considerations for multi-server setups:

• Token budget: Each server registers its tools on connect, consuming tokens. Three servers might use 2,000-5,000 tokens just for tool descriptions before any actual queries.
• Startup time: Each npx server takes 1-3 seconds to start. With three servers, expect 5-10 seconds before all tools are available.
• Naming conflicts: Some servers expose tools with generic names like query. The AI client handles this by prefixing the server name, but verify your client supports this.
• Memory usage: Each npx process runs a separate Node.js instance. Three database servers will consume roughly 200-400MB of RAM total.

Common Pitfalls When Connecting AI to Databases

After working with hundreds of developers setting up database MCP servers, these are the problems that come up again and again:

1. Schema Too Large for Context Window

If your Postgres database has 300+ tables, the schema information alone can consume 10,000+ tokens. The AI model's context window fills up with table descriptions, leaving less room for actual reasoning. Solutions:

• Create a dedicated Postgres schema (e.g., ai_accessible) containing only the tables AI needs, and restrict the MCP user to that schema
• Use views to present simplified versions of complex tables
• Some MCP servers support a --schema flag to limit introspection to specific schemas

2. AI Generating N+1 Queries

AI models sometimes take an iterative approach: query one table, then for each result, query another table. This creates N+1 query patterns that overwhelm the database. For example, the AI might first run SELECT id FROM orders WHERE status = 'pending', then run a separate SELECT * FROM order_items WHERE order_id = X for each result. Solution: prompt the AI to use JOINs, or set a per-session query count limit on your database role.

3. Credential Exposure in Config Files

The claude_desktop_config.json file stores connection strings in plain text. If you commit this file to version control or share your screen during a demo, your database credentials are exposed. Always use environment variables:

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres"
      ],
      "env": {
        "DATABASE_URL": "postgresql://readonly:pass@localhost:5432/myapp"
      }
    }
  }
}

Better yet, use a secrets manager or .env file that is excluded from version control. See our security fundamentals guide for more approaches.

4. Query Timeouts and Runaway Queries

AI-generated queries are not optimized by default. A query like SELECT * FROM events on a table with 50 million rows will try to return everything. Protect yourself:

• Postgres: ALTER ROLE mcp_user SET statement_timeout = '10s';
• MongoDB: Use --maxTimeMS 10000 in the server configuration
• Elasticsearch: Set timeout in search requests (most MCP servers do this by default)
• Always set a LIMIT or "size" on queries - some MCP servers enforce this automatically, but not all

5. Stale Schema Cache

Database MCP servers typically cache schema information at startup. If you run a migration while the MCP server is running, the AI will have outdated schema information and may generate invalid queries. Restart the MCP server after any schema change, or use clients that support a "reconnect" command.

6. Missing Indexes Causing Slow AI Queries

When AI explores your data, it often queries columns that your application never queries directly - for example, filtering users by created_at ranges or searching text fields. These columns might lack indexes. Monitor your slow query log after enabling AI access and add indexes for commonly AI-queried columns.

Monitoring AI Database Access

Once AI has access to your databases, you need visibility into what it's doing. Two MCP servers are particularly useful here:

Grafana MCP for Query Monitoring

The Grafana MCP server can query your existing Postgres monitoring dashboards. Set it up alongside your database MCP server and ask AI things like "Show me the slow query log from the last hour" or "What's the current connection pool utilization?" This creates a feedback loop where AI can see the impact of its own queries.

Datadog MCP for Production Observability

The Datadog MCP server gives AI access to your APM data. Combined with database MCP servers, you can ask: "Find the database query causing the latency spike on the /api/users endpoint." The AI can correlate application traces (from Datadog) with database schema and query execution (from the database MCP server) to identify root causes.

Audit Logging Configuration

For compliance and security, enable audit logging on your database side:

-- PostgreSQL: Log all queries from the MCP user
ALTER ROLE mcp_readonly SET log_statement = 'all';

-- Or use pgAudit for structured audit logs
CREATE EXTENSION pgaudit;
ALTER ROLE mcp_readonly SET pgaudit.log = 'read';

This gives you a complete record of every query AI executes through the MCP server, which is essential for debugging, compliance, and understanding how AI interacts with your data over time.

Security Considerations

Connecting AI to databases requires careful security planning. Read our MCP Security Guide for detailed best practices. Key recommendations:

• Always use read-only connections when possible - create a dedicated database role with SELECT-only permissions
• Never expose production database credentials in client configs - use environment variables or secrets managers
• Restrict schema access - grant the MCP user access only to the tables and schemas AI actually needs
• Enable audit logging to track every AI-generated query
• Set query timeout limits to prevent long-running operations: 10 seconds is a good default
• Use network-level restrictions - the MCP database user should only be able to connect from localhost or specific IPs
• Rotate credentials regularly and use short-lived tokens where possible (e.g., IAM authentication for AWS RDS)

Getting Started

Ready to connect AI to your database? Here's the fastest path:

• New to MCP? Start with our What is MCP? tutorial
• Want to try it now? The SQLite server requires zero setup - just point it at any .db file
• Building for production? Go with PostgreSQL and follow our security fundamentals guide
• Need all the options? Browse our full server directory filtered by the Databases category

What's Next

Database servers are just the beginning. Check out our guides on setting up MCP with your IDE, or learn how to build your own custom MCP server to connect to any data source. If you want to go deeper on keeping your database safe while AI accesses it, our security guide covers credential management, network isolation, and query sandboxing in detail.