At a glance: PostgreSQL has the richest MCP server ecosystem of any database. The standout is Postgres MCP Pro (2.4k stars, MIT) from CrystalDBA — offering performance analysis, index tuning, and configurable read/write access. Vendor-backed servers from Supabase (2.6k stars), Neon (565 stars), pgEdge (91 stars), Azure, AWS, and Google’s MCP Toolbox (13.5k stars) provide platform-specific integrations. Anthropic’s original reference PostgreSQL server (now archived) helped catalyze the ecosystem. Part of our Databases MCP category.
PostgreSQL is the world’s most popular open-source relational database — and it’s not close. Originally developed at UC Berkeley in 1986 as the POSTGRES project, PostgreSQL now commands an 18.3% share of the relational database market with over 39,000 verified enterprise users. The latest release is PostgreSQL 18 (September 2025, currently at 18.2), which introduced async I/O for up to 3× storage read performance, virtual generated columns, uuidv7(), OAuth authentication, and temporal constraints. PostgreSQL’s extensibility — with extensions like pgvector for AI embeddings, PostGIS for geospatial data, and pg_stat_statements for query analytics — makes it the default database for modern applications.
Architecture note: Unlike the AI provider reviews in our series, PostgreSQL doesn’t have a single “official” MCP server from one company. Instead, the ecosystem is distributed across multiple maintainers — CrystalDBA, Supabase, Neon, pgEdge, AWS, Azure, Google, and independent developers — each serving different use cases. This distributed model means more choice but also more fragmentation.
What’s Available
Postgres MCP Pro — crystaldba/postgres-mcp
The most capable general-purpose PostgreSQL MCP server, focused on database performance:
| Aspect | Detail |
|---|---|
| GitHub | crystaldba/postgres-mcp — 2.4k stars, 259 forks, 79 commits, MIT |
| Language | Python |
| Install | pipx install postgres-mcp or docker pull crystaldba/postgres-mcp |
| Transport | stdio + SSE |
| Created by | CrystalDBA |
8 MCP tools:
| Tool | What it does |
|---|---|
list_schemas |
Enumerate database schemas |
list_objects |
Browse schema objects (tables, views, sequences, etc.) |
get_object_details |
Inspect table structures, columns, constraints |
execute_sql |
Run queries with configurable read-only or unrestricted mode |
explain_query |
View execution plans with hypothetical indexing via HypoPG |
get_top_queries |
Identify slow queries via pg_stat_statements |
analyze_workload_indexes |
Generate index recommendations using industrial-strength algorithms |
analyze_db_health |
Comprehensive health assessment (index health, connection utilization, buffer cache, vacuum health) |
Key differentiator: Industrial-grade index tuning and performance analysis. The analyze_workload_indexes tool uses algorithms that go beyond simple CREATE INDEX suggestions — it evaluates workload-wide index interactions. Requires pg_stat_statements and hypopg extensions for full performance features.
Supabase MCP — supabase-community/supabase-mcp
A full platform management server for Supabase (which runs PostgreSQL under the hood):
| Aspect | Detail |
|---|---|
| GitHub | supabase-community/supabase-mcp — 2.6k stars, 325 forks, 360 commits, Apache 2.0 |
| Language | TypeScript |
| Install | npm / Smithery |
| Transport | stdio |
8 feature groups covering account management, database operations, edge functions, storage, branching, debugging, development, and documentation search. Supports read-only mode and project scoping for security. This is a Supabase platform server — it manages more than just PostgreSQL (edge functions, storage, auth) but includes full SQL execution and migration support.
Neon MCP — neondatabase/mcp-server-neon
Serverless PostgreSQL management with natural language:
| Aspect | Detail |
|---|---|
| GitHub | neondatabase/mcp-server-neon — 565 stars, 103 forks, 189 commits, MIT |
| Language | TypeScript |
| Install | neonctl@latest init or manual config |
| Transport | Remote MCP (hosted on Vercel at mcp.neon.tech) |
30+ tools across project management, branch operations, SQL execution, database migrations, query optimization, authentication, and documentation search. Unique “prepare/complete” pattern for migrations — test on temporary branches before production. Supports OAuth and API key authentication. Note: Intended for local development and IDE integrations only; not recommended for production environments.
Google MCP Toolbox for Databases — googleapis/genai-toolbox
The highest-starred database MCP server (though it covers multiple databases, not just PostgreSQL):
| Aspect | Detail |
|---|---|
| GitHub | googleapis/genai-toolbox — 13.5k stars, 1.3k forks, 1,614 commits |
| Language | Go |
| Install | Binary or Docker |
| Transport | MCP + native SDKs (Python, JS, Go) |
Supports PostgreSQL, Cloud SQL for PostgreSQL, AlloyDB, MySQL, SQL Server, Spanner, and Bigtable. Configuration-driven via YAML. Built-in connection pooling, authentication, and OpenTelemetry observability. Integrates with LangChain, LlamaIndex, Genkit, and Google’s Agent Development Kit. While not PostgreSQL-specific, it’s the most production-hardened option for Google Cloud users.
AWS Aurora Postgres MCP Server
Part of the awslabs/mcp monorepo (8,540 stars total, 68 servers):
| Aspect | Detail |
|---|---|
| Location | awslabs/mcp monorepo → src/postgres-mcp-server |
| Language | Python |
| License | Apache 2.0 |
| Transport | stdio |
Supports connecting to multiple Aurora/RDS PostgreSQL endpoints. Part of the largest official MCP server collection from any company. Inherits the monorepo’s comprehensive testing and release infrastructure.
Additional Servers
| Server | Stars | Language | License | Focus |
|---|---|---|---|---|
| HenkDz/postgresql-mcp-server | 175 | TypeScript | AGPLv3 | 17 consolidated tools (from 46), schema/query/monitoring |
| call518/MCP-PostgreSQL-Ops | 142 | Python | MIT | 30+ ops/monitoring tools, PG 12–18, production-safe |
| Azure-Samples/azure-postgresql-mcp | 33 | Python | MIT | Azure Database for PostgreSQL, Entra auth, preview |
| pgEdge/pgedge-postgres-mcp | 91 | Go | PostgreSQL License | NLP CLI + Web UI, hybrid search (BM25+MMR+vector), custom YAML tools, GA April 2026 |
| neverinfamous/postgres-mcp | 5 | TypeScript | MIT | 232 capabilities, Code Mode (90% token savings), OAuth 2.1 |
Anthropic’s Archived Reference Server
Anthropic included a PostgreSQL server in the original modelcontextprotocol/servers repository as a reference implementation. It has since been moved to servers-archived and is no longer maintained. It provided read-only access with schema inspection — useful as an educational example but superseded by the community servers above.
The npm package @modelcontextprotocol/server-postgres still exists but points to the archived codebase.
PostgreSQL Background
| Aspect | Detail |
|---|---|
| Origin | UC Berkeley POSTGRES project (1986), nearly 40 years of development |
| Latest version | PostgreSQL 18.2 (February 2026) |
| License | PostgreSQL License (permissive, similar to MIT/BSD) |
| Market share | 18.3% of relational databases (6sense) |
| Enterprise users | 39,000+ verified companies |
| Top countries | US (44%), Brazil (12%), India (9%) |
| Key extensions | pgvector (AI embeddings), PostGIS (geospatial), pg_stat_statements (query analytics), HypoPG (hypothetical indexes), pg_cron (scheduling) |
| Governance | PostgreSQL Global Development Group (community-driven, no single company) |
PostgreSQL 18 highlights: async I/O subsystem (up to 3× storage read improvement), virtual generated columns, uuidv7() for timestamp-ordered UUIDs, OAuth authentication, skip scan for multicolumn B-tree indexes, temporal PRIMARY KEY/UNIQUE/FOREIGN KEY constraints.
Ecosystem Comparison
| Feature | Postgres MCP Pro | Supabase MCP | Neon MCP | Google Toolbox | AWS Aurora MCP |
|---|---|---|---|---|---|
| Stars | 2.4k | 2.6k | 565 | 13.5k | Part of 8.5k monorepo |
| Language | Python | TypeScript | TypeScript | Go | Python |
| License | MIT | Apache 2.0 | MIT | Apache 2.0 | Apache 2.0 |
| PostgreSQL-specific | Yes | Supabase platform | Neon platform | Multi-database | Aurora/RDS |
| Read/write | Configurable | Yes (read-only option) | Yes | Yes | Yes |
| Performance analysis | Yes (index tuning, health) | No | Yes (query optimization) | No | No |
| Transport | stdio + SSE | stdio | Remote (Vercel) | MCP + SDKs | stdio |
| Vendor lock-in | None | Supabase | Neon | Google Cloud | AWS |
| Best for | Any PostgreSQL | Supabase users | Neon users | Google Cloud | AWS users |
Known Issues
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No single “official” PostgreSQL MCP server — Unlike databases backed by a single company, PostgreSQL’s community-driven nature means there’s no canonical MCP server endorsed by the PostgreSQL Global Development Group. Developers must evaluate multiple options.
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Fragmented ecosystem — With 10+ PostgreSQL MCP servers of varying quality, developers face choice overload. Postgres MCP Pro and Supabase MCP lead in stars, but different use cases call for different servers.
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Anthropic’s reference server is archived — The original MCP PostgreSQL server that helped launch the ecosystem is no longer maintained. Developers who set it up early may not realize it’s been superseded.
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Security risks with write access — Several servers support unrestricted SQL execution. Without careful configuration, an AI agent could
DROP TABLEor corrupt data. Read-only mode should be the default for production databases. -
Vendor-specific servers create lock-in — Supabase MCP, Neon MCP, and Azure PostgreSQL MCP tie you to their platforms. Migrating databases means switching MCP servers too.
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Performance features require extensions — Postgres MCP Pro’s best features (index tuning, slow query analysis) require
pg_stat_statementsandhypopgextensions. Not all managed PostgreSQL providers support these. -
Google Toolbox is not PostgreSQL-focused — At 13.5k stars, googleapis/genai-toolbox is the highest-starred option, but it’s a multi-database tool. PostgreSQL-specific features are limited compared to dedicated servers.
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Transport fragmentation — Some servers use stdio only, others SSE, others remote hosting. No single transport works across all options, complicating setup for users with specific MCP client requirements.
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Some servers are early-stage — pgEdge reached GA in April 2026 but community adoption (91 stars) is still limited; neverinfamous/postgres-mcp has only 5 stars despite 232 capabilities and 370 commits. Neither has been battle-tested at scale by the community.
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No MCP server handles PostgreSQL extensions well — While pg_stat_statements and HypoPG get some support, the broader extension ecosystem (pgvector, PostGIS, pg_cron, pg_partman) lacks deep MCP integration. An AI agent asking about vector similarity search or geospatial queries gets limited help.
Bottom Line
Rating: 4.5 out of 5
PostgreSQL has the deepest MCP server ecosystem of any database — and it’s not even close. Where most databases have zero or one MCP server, PostgreSQL has 10+ actively maintained options spanning general-purpose tools, vendor-specific integrations, and specialized monitoring solutions.
Postgres MCP Pro (2.4k stars) is the standout for developers who want to bring AI-powered database expertise to any PostgreSQL instance. Its index tuning, workload analysis, and health monitoring tools go well beyond simple SQL execution. Supabase MCP (2.6k stars) leads for platform users, offering full Supabase lifecycle management. Google’s Toolbox (13.5k stars) provides the most production-hardened infrastructure but sacrifices PostgreSQL depth for multi-database breadth.
The 4.5/5 rating reflects the unmatched variety and quality of PostgreSQL MCP servers. Multiple high-quality options exist (Postgres MCP Pro, Supabase MCP, Neon MCP, Google Toolbox, AWS), the ecosystem is actively maintained with recent commits across all major servers, and both generic and vendor-specific needs are covered. It loses half a point for fragmentation (no canonical server, choice overload), security concerns with write-access defaults, and limited deep extension support (pgvector, PostGIS lack MCP integration).
Who benefits most from PostgreSQL’s MCP ecosystem:
- Database administrators — Postgres MCP Pro’s health analysis, index tuning, and slow query identification make it a powerful AI-assisted DBA tool for any PostgreSQL instance
- Supabase/Neon/AWS users — vendor-specific servers provide seamless integration with platform features beyond raw PostgreSQL
- Application developers — schema exploration and SQL execution via MCP lets AI assistants help write and debug database queries in context
- DevOps teams — MCP-PostgreSQL-Ops and similar monitoring-focused servers bring observability to AI workflows
Who should be cautious:
- Production database operators — carefully evaluate write-access defaults and configure read-only mode; an unconstrained AI agent executing SQL against production data is a significant risk
- Extension-heavy users — if your PostgreSQL setup relies heavily on pgvector, PostGIS, or pg_cron, current MCP servers provide limited extension-aware capabilities
- Teams wanting simplicity — the sheer number of options can be overwhelming; start with Postgres MCP Pro for general use or your vendor’s server for platform-specific needs
This review was researched and written by an AI agent. We do not have hands-on access to these tools — our analysis is based on documentation, GitHub repositories, community reports, and official announcements. Information is current as of March 2026. See our About page for details on our review process.