MindsDB
A federated query engine that exposes 200+ data sources as MCP tools through a unified SQL-compatible interface. Built around a Connect → Unify → Respond workflow with structured tables fused with vectorized data inside Knowledge Bases.
“MindsDB is a different shape from every other server in this directory. Most MCPs are thin wrappers around one product's API; MindsDB is a query engine that federates 200+ data sources (Postgres, MongoDB, Slack, Salesforce, Shopify, files, and so on) and exposes them as a single SQL-compatible surface to the agent. The pitch is "give your agent access to all your live data through one connection," and the architecture delivers it: the agent issues SQL, MindsDB routes the query across the federation, results stream back. The cost is operational complexity. Running MindsDB means standing up a Docker container or PyPI install with a database backend, configuring connectors per data source, and managing a Knowledge Base for the unstructured side. Teams who already use MindsDB for analytics get the MCP integration nearly for free; teams considering MindsDB only for the agent surface should weigh the install footprint against using individual MCP servers per data source.”
INSTALL THIS SERVER
{
"mcpServers": {
"mindsdb": {
"command": "npx",
"args": [
"mcp-remote",
"http://localhost:47334/mcp"
]
}
}
}
{
"mcpServers": {
"mindsdb": {
"command": "npx",
"args": [
"mcp-remote",
"http://localhost:47334/mcp"
]
}
}
}
{
"mcpServers": {
"mindsdb": {
"command": "npx",
"args": [
"mcp-remote",
"http://localhost:47334/mcp"
]
}
}
}
{
"mcpServers": {
"mindsdb": {
"command": "npx",
"args": [
"mcp-remote",
"http://localhost:47334/mcp"
]
}
}
}
{
"mcpServers": {
"mindsdb": {
"command": "npx",
"args": [
"mcp-remote",
"http://localhost:47334/mcp"
]
}
}
}
6 TOOLS AVAILABLE
OUR ASSESSMENT
- 200+ live data source connectors out of the box; no per-source server install required.
- Knowledge Base layer fuses structured tables with vectorized text, PDFs, and HTML for semantic search.
- SQL-compatible language with workflow constructs (jobs, triggers) for agent automation.
- Active commercial development with sustained release cadence; 39k stars reflect the underlying product's reach.
- Significant install footprint compared to a single-purpose MCP server; requires Docker or Python runtime plus configuration per data source.
- Elastic License 2.0 (applied to MindsDB Core) places restrictions on managed-service redistribution; teams running MindsDB as a hosted service for others need to read the license terms carefully. The mindsdb/integrations directory is MIT-licensed.
- The MCP surface is one of many integrations; primary product positioning is "AI analytics platform," and some MCP-specific features lag the core product.
MindsDB acts as a credential broker for every connected data source. Each data source connection holds its own credentials (database connection strings, API keys, OAuth tokens) which are stored in the MindsDB instance's configuration. Securing the MindsDB instance is securing the credential vault for the federation. Production deployments use environment-variable-based config and run MindsDB behind a network boundary. The MCP exposure inherits whatever permissions the data source connections carry, so scope each connector's credentials carefully.
Teams already running MindsDB for analytics, or teams who need an agent to reason across many heterogeneous data sources through one query layer.
TECHNICAL DETAILS
ADOPTION METRICS
// Reading this39k stars reflect the broader MindsDB project, of which the MCP surface is one integration. Star traction predates MCP and signals the maturity of the underlying federation engine.
// Reading thisTop-ranked database MCP by raw star count. The federation breadth is the differentiator, weighed against per-connector setup overhead.
SOURCES & VERIFICATION
We don't take any single directory's word for it. Before scoring, we cross-reference 4 public MCP sources, install the server ourselves against the clients we cover, and record when we last re-verified.
The same server, 4 different lenses. We reconcile these signals into our editorial score, which is why our number sometimes diverges from a directory-aggregate star count.
| Source | Their rating | Their star count | Their downloads | Last synced |
|---|---|---|---|---|
| AutomationSwitch This page | 4editorial | 39,077 | — | APR 28, 2026 |
| PulseMCP | — unrated | unavailable | unavailable | APR 28, 2026 |
| MCPMarket | — unrated | unavailable | unavailable | APR 28, 2026 |
| Official MCP Registry | — unrated | unavailable | unavailable | APR 28, 2026 |
| Awesome MCP Servers | — unrated | unavailable | unavailable | APR 28, 2026 |
// Counts are directory-reported; we don't adjust them. Discrepancies usually come from different snapshot times or star-caching.
OTHER DATABASE MCP SERVERS
Postgres MCP
Official MCP server for PostgreSQL. Provides read and write access to PostgreSQL databases with schema introspection, query execution, and transaction support. The reference implementation for database MCP servers.
Supabase
Persistence layer with full Postgres access and Row Level Security awareness. Query tables, manage schemas, handle auth users, and work with storage buckets.
Neon MCP
MCP server for Neon serverless Postgres. Provides database branching, query execution, and project management. Branch-per-query makes every schema change reversible.
MongoDB MCP Server
Vendor-built MongoDB MCP server covering both direct database operations (against any MongoDB connection string) and MongoDB Atlas API operations (via Service Accounts credentials). Ships with --readOnly enabled by default in every official install snippet.
Neo4j MCP (Labs)
Four Neo4j MCP servers maintained by the Neo4j Field GenAI team as part of Neo4j Labs: cypher (natural-language-to-Cypher with schema introspection), memory (knowledge-graph memory across agent sessions), cloud-aura-api (Aura cloud instance management), and data-modeling (graph data modelling and visualization). Each is a separate installable PyPI package.
DISCUSS YOUR
MCP REQUIREMENTS.
Evaluating a server, scoping an internal deployment, or working out whether MCP is the right fit at all. Start the conversation and we will point you at the right piece of the ecosystem.