Best Database MCP Servers in 2026: 10 Picks for Postgres, Mongo, Vector + more

TL;DR + decision tree

There is no “best database MCP server” in the abstract — there is the right one for the database you run. Start from your data model:

• Already on Supabase? Supabase MCP — reaches Postgres plus storage, edge functions, and branching from one server, with read-only and project-scoped modes.
• Plain PostgreSQL, just let the agent read it? Postgres MCP — connect, inspect schemas, run SQL. The fastest path.
• Postgres performance is the job? Postgres MCP Pro — index tuning, EXPLAIN-plan analysis, and health checks, with a restricted read-only mode for production.
• MySQL? MySQL MCP — execute queries and manage connections, results back as JSON.
• Several SQL engines, one connector? DBHub — a token-efficient universal gateway across Postgres, MySQL, SQL Server, MariaDB, and SQLite.
• Document data? MongoDB MCP — collection management, schema inspection, and Atlas operations.
• Event-scale analytics? ClickHouse MCP for OLAP at cluster scale, or DuckDB MCP for local, file-based analytical SQL.
• Graphs and relationships? Neo4j MCP — natural-language and Cypher queries over a graph database.
• Vector search for RAG? Pinecone MCP — embeddings and similarity search. For the full vector field, see the dedicated head-to-head below.

If your decision is specifically about vector stores, this listicle keeps Pinecone short on purpose — the four-way fight lives in Chroma vs Pinecone vs Qdrant vs Weaviate vs pgvector. This page is the wide-angle map across every data model.

What a database MCP server actually does

A database MCP server is a thin connector that exposes your database as tools an AI agent can call. MCP (the Model Context Protocol) is the open standard Claude and other clients use to talk to external systems, so a database MCP gives the model a structured way to list tables, read a schema, and run a query — no CSV exports, no pasting result sets back into chat. The data stays in your database; the server just makes it callable.

The reason there are ten servers here rather than one is that databases are not interchangeable. A row-oriented OLTP store like Postgres answers “what is order #4471?” instantly but crawls on “sum revenue by region for the last year.” A column-oriented OLAP engine like ClickHouse is the reverse. A graph database models relationships a join cannot reach; a vector database retrieves by similarity, not equality. The MCP server inherits its database’s strengths — so you pick the server that matches where the answer actually lives. New to the protocol? The What is MCP primer covers the wire format these connectors speak.

One boundary worth stating up front: these are query and inspection tools, not a replacement for migrations, backups, or your ORM. Most default to read-only or ship a read-only mode, and that is the setting you want pointed at production. The write-capable modes exist for local development, where an agent scaffolding a schema is genuinely useful.

Side-by-side matrix

Sourced from each server’s repo and its catalog entry, checked 2026-06-23. “Read-only” reflects the default or available safe mode; several servers also offer an unrestricted write mode for development.

Two patterns fall out of the table. First, the data model is the real axis of choice — four of these are relational and overlap heavily, while ClickHouse, Neo4j, and Pinecone exist precisely because their model does something SQL cannot. Second, read-only is the common safety story: every server here either defaults to reads or ships an explicit read-only mode, which is the setting you keep on in production regardless of which one you choose. The rest of this guide goes server by server, grouped by model. Browse the full set anytime at /categories/databases — 130-plus servers and counting.

Supabase MCP — install + recipe

Supabase MCP Serversupabase-community

Official1-Click ReadyRemote

Connect Supabase projects to AI with Supabase MCP Server. Standardize LLM communication for secure, efficient development and data management.

databases2.6k47

What it does best

The Supabase server is the only relational pick here that reaches past the database into the whole platform: alongside table and query operations it touches storage, edge functions, project branching, and documentation search. If your appis Supabase, one server gives the agent the same surface you use in the dashboard — manage tables, query data, spin up a branch, debug a deploy — instead of stitching three connectors together.

Pick this if you...

• Build on Supabase and want the agent to manage schema, storage, and edge functions from one place
• Want safe defaults — read-only and project-scoped modes are built in, so a misfired prompt can’t mutate prod
• Use branching and want an agent to create or inspect a preview branch as part of a change

Recipe: scope a feature against your schema

In any MCP client connected to Supabase MCP, in read-only mode:

Inspect the public schema. I want to add a "saved searches"
feature — list the tables and foreign keys involved in users
and searches, then propose the new table + RLS policies, and
flag any column I'd need to backfill.

The agent reads the live schema, traces the foreign keys, and drafts the migration plus row-level-security policies grounded in what already exists — not a generic guess. For the deep dive on this server specifically, see our Supabase MCP complete guide.

Skip it if...

You run Postgres anywhere other than Supabase — most of the platform tools won’t apply, and the plain Postgres server or Postgres MCP Pro fits a self-hosted or RDS database better.

Postgres MCP — install + recipe

What it does best

This is the no-friction option: connect to any PostgreSQL database, inspect schemas, and run SQL. It does one thing — give an agent read access to your tables — and gets out of the way. When all you want is “let Claude answer questions about my data,” this is the shortest distance between here and there, with nothing to learn beyond a connection string.

Pick this if you...

• Want the fastest possible setup — a connection string and you’re querying
• Need an agent to explore an unfamiliar Postgres schema and explain how the tables relate
• Don’t need tuning or health analytics — just clean read access to data

Recipe: explain an unfamiliar schema

In any MCP client connected to Postgres MCP:

List every table in this database with its row count estimate.
Then for the 5 largest, describe what each appears to store,
its primary key, and which other tables reference it. Draw the
relationships as a simple text ER outline.

The agent enumerates tables, pulls schema and key metadata, and builds a relationship map — the orientation pass you’d otherwise do by hand on a database you inherited. A close cousin is the PostgreSQL skill, which packages a repeatable workflow on top of whichever Postgres connection you wire in.

Skip it if...

You need performance work — slow-query analysis, index recommendations, EXPLAIN plans. That’s Postgres MCP Pro, next. This server reads; it doesn’t advise.

Postgres MCP Pro — install + recipe

Postgres MCP Procrystaldba

1-Click Ready

Boost Postgres performance with Postgres MCP Pro—AI-driven index tuning, health checks, and safe, intelligent SQL optimization.

databases66411

What it does best

Postgres MCP Pro turns the agent into a performance engineer. Its index tuning explores thousands of candidate indexes with industrial-strength algorithms; its query-plan tools validate and simulate the impact of hypothetical indexes via EXPLAIN; and its health checks read index health, buffer cache, vacuum health, connection utilization, and replication lag. The restricted mode limits the agent to read-only transactions with execution-time caps — exactly what you want against a production database.

Pick this if you...

• Are chasing a slow query and want index recommendations backed by cost-benefit analysis, not vibes
• Run Postgres on RDS, Cloud SQL, Azure, or self-hosted and need a database-health read before a launch
• Want a hard read-only guardrail with execution-time limits when pointing an agent at prod

Recipe: diagnose a slow endpoint

In any MCP client connected to Postgres MCP Pro, restricted mode:

Find the top 10 slowest queries by total time. For the worst
one, show its EXPLAIN plan, then recommend indexes — simulate
each candidate and report the projected cost change before/after.
Finish with a database health summary (cache hit rate, vacuum,
connection use).

The agent pulls the slow-query list, runs plan analysis, simulates indexes against the real workload, and returns a ranked recommendation plus a health snapshot — the analysis a DBA would bill an afternoon for, in one prompt.

Skip it if...

You only need to read data, not tune it — the plain Postgres server is lighter and simpler. The Pro feature set is wasted if performance isn’t the question.

MySQL MCP — install + recipe

MySQL Databaseburakdirin

1-Click Ready

Connect to MySQL databases, execute SQL queries, and get JSON results easily with our simple interface—ideal for MySQL and MySQL Workbench users.

databases1.2k16

What it does best

The MySQL server is the MySQL counterpart to the plain Postgres pick: a direct line for executing SQL and managing connections, with results handed back as JSON the agent can reason over. It’s the right home base for the large share of applications that still run on MySQL or MariaDB and want an agent that can answer questions against the live database.

Pick this if you...

• Run a MySQL or MariaDB application and want conversational query access without a dashboard
• Work in MySQL Workbench and want the agent to draft and run the query you were about to write
• Prefer JSON-shaped results the model can summarize and chart in the next turn

Recipe: ad-hoc reporting query

In any MCP client connected to MySQL MCP:

Against the orders and customers tables, write and run a query
for the top 20 customers by total spend in the last 90 days,
including order count and average order value. Return JSON, then
summarize the three takeaways in plain English.

The agent inspects the relevant tables, composes the join and aggregation, runs it, and turns the JSON into a short readout — the reporting question that usually means a round trip through a BI tool, answered inline.

Skip it if...

You manage several different SQL engines and don’t want a server per database. DBHub covers MySQL alongside Postgres, SQL Server, MariaDB, and SQLite from one connector — that’s next.

DBHub — install + recipe

DBHub (Universal Database Gateway)bytebase

Official1-Click Ready

DBHub: Universal database gateway to view SQLite database, run sequel queries & browse tables. Secure, safe, and easy-to-use SQLite browser.

databasesdeveloper tools1.2k47

What it does best

DBHub is the universal gateway: one connector reaching PostgreSQL, MySQL, SQL Server, MariaDB, and SQLite. Its design choice is the interesting part — it exposes just two tools (schema search and SQL execution) specifically to keep the agent’s context window lean, and it’s described as a zero-dependency, token-efficient server. Read-only mode, row limiting, and query timeouts are built in as guardrails against runaway operations.

Pick this if you...

• Touch several SQL engines and want one server instead of five cluttering every prompt
• Care about context efficiency — two tools means the agent isn’t wading through dozens of descriptions
• Want built-in read-only, row caps, and timeouts as a default safety posture

Recipe: cross-engine schema audit

In any MCP client connected to DBHub:

Connected to our reporting Postgres and the legacy MySQL.
Search both schemas for tables that look like they store user
email addresses. List each table, the column, and which engine
it's on, so I can map our PII surface. Read-only.

The agent runs schema search against both connections, matches candidate columns, and assembles a single inventory across engines — the kind of audit that’s tedious precisely because the data lives in two different databases. GitHub ranks DBHub (from Bytebase) among the most-starred database MCP servers, and the token-efficiency angle is why.

Skip it if...

You want deep, engine-specific capability — index tuning on Postgres, Atlas operations on MongoDB. A gateway trades depth for breadth; for one database you’ll live in, a dedicated server gives you more.

MongoDB MCP — install + recipe

MongoDBmongodb-js

Official1-Click Ready

Connect MongoDB databases to chat interfaces. Manage AWS with MongoDB, explore Atlas cost, and inspect collections securely with authentication.

databases62114

What it does best

The MongoDB server bridges document data to a conversational interface: collection management, schema inspection over flexible documents, and Atlas cloud operations from the same connector. Schema inspection matters more here than in the SQL world — Mongo documents don’t advertise their shape, so having an agent sample collections and infer structure is genuinely useful before you write a query.

Pick this if you...

• Store data in MongoDB and want an agent that can navigate collections without a rigid schema to lean on
• Run on Atlas and want cluster and cost operations alongside data queries
• Need the agent to infer document shape by sampling before composing an aggregation

Recipe: infer shape, then aggregate

In any MCP client connected to MongoDB MCP:

Sample 20 documents from the "events" collection and infer the
field structure, including nested objects. Then build an
aggregation pipeline that counts events by type per day for the
last week, and run it.

The agent samples documents, reconstructs the (often nested) schema, and writes the aggregation pipeline against the real shape — closing the gap that makes ad-hoc Mongo queries harder than their SQL equivalents.

Skip it if...

Your data is relational. Document modeling and SQL are different worlds; for tables and joins, the Postgres or MySQL servers are the right fit.

ClickHouse MCP — install + recipe

ClickHouseClickHouse

Official1-Click Ready

Unlock powerful OLAP database analytics on ClickHouse MCP Server. Manage OLAP data with seamless online analytical processing and robust workflows.

databases7106

What it does best

ClickHouse is the OLAP specialist: its server runs SQL across ClickHouse clusters or the embedded chDB engine, built for scanning enormous event tables fast. Where a row-oriented database grinds on “aggregate a billion rows by dimension,” ClickHouse is engineered for exactly that — so an agent answering analytical questions over logs, clickstream, or metrics belongs here, not on your OLTP database.

Pick this if you...

• Keep event, log, or analytics data at a scale where aggregate queries are the whole point
• Want the agent to explore a warehouse without dragging huge scans onto your transactional database
• Use chDB and want embedded OLAP over files without standing up a cluster

Recipe: cohort question over event data

In any MCP client connected to ClickHouse MCP:

List the tables in the analytics database. Against the events
table, compute weekly active users for the last 8 weeks and the
week-over-week retention of the cohort that first appeared 8
weeks ago. Return a tidy table.

The agent discovers the schema, writes the windowed aggregation ClickHouse is fast at, and returns the cohort table — an analytics question that would either time out or melt a transactional database, answered in seconds.

Skip it if...

Your workload is transactional — single-row reads and writes, app state. OLAP engines are built for scans, not point lookups; a Postgres or MySQL server fits that shape far better.

DuckDB MCP — install + recipe

DuckDBktanaka101

1-Click Ready

Execute SQL queries and analyze data efficiently in DuckDB databases. Unlock powerful analytics with DuckDB.

databasesanalytics data5893

What it does best

DuckDB is analytical SQL with zero infrastructure: an in-process engine the server points at local files — Parquet, CSV, a DuckDB database — so the agent runs OLAP-style queries without a server to provision. It’s the analyst’s scratchpad: drop a data file in, ask aggregate questions, get answers, nothing to deploy or pay for.

Pick this if you...

• Have data sitting in Parquet or CSV files and want analytical SQL over it without a database server
• Do local exploration and prototyping where standing up ClickHouse would be overkill
• Want a fast, embedded engine for one-off analysis the agent can run on your machine

Recipe: query a Parquet file in place

In any MCP client connected to DuckDB MCP:

Read ./exports/transactions.parquet directly. Show the schema,
then compute monthly revenue and the top 5 product categories by
margin. No need to load it into a table first — query the file.

The agent reads the file’s schema, queries it in place with DuckDB’s native Parquet support, and returns the rollups — the “I just need to slice this export” task that usually means a throwaway notebook. The DuckDB skill guide shows ten more workflows in this shape.

Skip it if...

You need a shared, always-on warehouse multiple services query. DuckDB is embedded and single-process by design; for cluster scale and concurrency, ClickHouse is the analytical pick.

Neo4j MCP — install + recipe

Neo4jda-okazaki

1-Click Ready

Integrate with Neo4j graph database for natural language queries, node creation, and advanced data relationships in graph db Neo4j.

databasesanalytics data916

What it does best

Neo4j is the graph pick: the server lets an agent query a graph database in natural language or Cypher, create nodes, and walk relationships. The case for it is the questions a join can’t answer cleanly — “who is connected to whom, three hops out,” recommendation paths, fraud rings, knowledge graphs. When the relationships are the data, a graph store models them natively and the agent traverses them instead of writing five self-joins.

Pick this if you...

• Model a domain as a graph — social, supply chain, knowledge base, dependency network
• Ask multi-hop relationship questions that turn into painful recursive SQL on a relational database
• Want the agent to translate a plain-English question into Cypher rather than hand-writing traversals

Recipe: multi-hop relationship query

In any MCP client connected to Neo4j MCP:

In our knowledge graph, find all Companies connected to
"Acme Corp" through shared Investors or shared Board members,
up to 3 hops away. Return the path for each, ranked by number
of connections, as Cypher results.

The agent composes the Cypher traversal, runs it, and returns the connection paths — the kind of relationship query that is a one-liner in a graph database and a nightmare of recursive CTEs in SQL.

Skip it if...

Your data is tabular and your questions are aggregations or lookups. A graph database is the wrong tool for rows and columns; reach for a relational or analytical server instead.

Pinecone MCP — install + recipe

Pinecone Developer (Vector Database)pinecone-io

Official1-Click Ready

Build with Pinecone, the vector database designed for scalable, knowledgeable AI. Try Pinecone vector database and excel in AI projects.

databases667

What it does best

Pinecone is the vector pick: a managed vector database for storing embeddings and retrieving by similarity, the retrieval layer behind retrieval-augmented generation. Its server lets an agent search an index for the most semantically similar records to a query — the “find the docs most relevant to this question” step that turns a pile of embeddings into grounded answers.

Pick this if you...

• Run RAG and want the agent to query your Pinecone index for relevant context before it answers
• Already host embeddings in Pinecone and want managed scale without operating the store yourself
• Need similarity search as a building block in a larger agent workflow

Recipe: grounded answer from your index

In any MCP client connected to Pinecone MCP:

Search the "docs" index for the 8 chunks most relevant to
"how do refunds work for annual plans?" Show each chunk's
source and score, then answer the question using only those
chunks and cite which ones you used.

The agent runs the similarity search, ranks the matches, and composes a grounded, cited answer from them — the core RAG loop, in one prompt. Because vector stores are a crowded field, the full comparison lives in Chroma vs Pinecone vs Qdrant vs Weaviate vs pgvector — read it before you commit to one.

Skip it if...

You don’t have an embeddings workload, or you’d rather keep vectors next to your relational data — pgvector on Postgres is the “one fewer system” answer, covered in the vector comparison above.

Common pitfalls

Community signal

Database MCP servers show up constantly when developers share what actually earns a slot in their setup. In a 60-comment r/codex thread (300-plus upvotes), a full-stack developer with 15 years of experience put a Postgres server near the top of the list of MCP servers that “really boosted my productivity”:

That maps cleanly onto the split in this guide: the plain Postgres server for schema inspection and ad-hoc queries, and Postgres MCP Pro when “analyze queries” turns into serious index and performance work. On the engineering side, GitHub’s most-starred database MCP repos lean toward gateways and token-efficient designs — Bytebase’s DBHub describes itself as a “zero-dependency, token-efficient database MCP server,” which is the same context-budget concern that shapes which servers survive in a real client.

Frequently asked questions

Sources

Relational

• /servers/supabase-mcp-server · github.com/supabase-community/supabase-mcp
• /servers/postgres — plain PostgreSQL connector
• /servers/postgres-mcp-pro · github.com/crystaldba/postgres-mcp — index tuning, EXPLAIN, health, restricted mode
• /servers/mysql-database — MySQL query connector

Gateway, document, analytical, graph, vector

• /servers/dbhub-universal-database-gateway · github.com/bytebase/dbhub — zero-dependency, token-efficient, two tools
• /servers/mongodb · github.com/mongodb-js/mongodb-mcp-server
• /servers/clickhouse · github.com/ClickHouse/mcp-clickhouse — clusters + chDB
• /servers/neo4j — Cypher / natural-language graph queries
• /servers/pinecone-developer-vector-database · github.com/pinecone-io/pinecone-mcp
• /servers/duckdb · github.com/ktanaka101/mcp-server-duckdb

Community

• r/codex — “MCP servers that made your productivity to touch the sky” — 60-comment thread, Postgres quote above

Related on MCP.Directory

• Chroma vs Pinecone vs Qdrant vs Weaviate vs pgvector MCP — the vector-database head-to-head
• Supabase MCP complete guide — deep dive on the Supabase server
• Best Knowledge Base MCP Servers (2026) — the retrieval layer that pairs with vector data
• Claude DuckDB skill guide — ten analytical workflows on DuckDB
• /categories/databases — browse every database MCP server