README.md•11.2 kB
# PostgreSQL MCP Server
[](https://smithery.ai/server/@gldc/mcp-postgres)
<a href="https://glama.ai/mcp/servers/@gldc/mcp-postgres">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@gldc/mcp-postgres/badge" />
</a>
A PostgreSQL MCP server implementation using the [Model Context Protocol (MCP)](https://github.com/modelcontextprotocol) Python SDK- an open protocol that enables seamless integration between LLM applications and external data sources. This server allows AI agents to interact with PostgreSQL databases through a standardized interface.
## Features
- List database schemas
- List tables within schemas
- Describe table structures
- List table constraints and relationships
- Get foreign key information
- Execute SQL queries
- Typed tools with JSON/markdown output
- Optional table resources and guidance prompts
## Quick Start
```bash
# Run the server without a DB connection (useful for Glama or inspection)
python postgres_server.py
# With a live database – pick one method:
export POSTGRES_CONNECTION_STRING="postgresql://user:pass@host:5432/db"
python postgres_server.py
# …or…
python postgres_server.py --conn "postgresql://user:pass@host:5432/db"
# Or using Docker (build once, then run):
# docker build -t mcp-postgres . && docker run -p 8000:8000 mcp-postgres
```
## Installation
### Installing via Smithery
To install PostgreSQL MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@gldc/mcp-postgres):
```bash
npx -y @smithery/cli install @gldc/mcp-postgres --client claude
```
### Manual Installation
1. Clone this repository:
```bash
git clone <repository-url>
cd mcp-postgres
```
2. Create and activate a virtual environment (recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows, use: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Start the MCP server.
```bash
# Without a connection string (server starts, DB‑backed tools will return a friendly error)
python postgres_server.py
# Or set the connection string via environment variable:
export POSTGRES_CONNECTION_STRING="postgresql://username:password@host:port/database"
python postgres_server.py
# Or pass it using the --conn flag:
python postgres_server.py --conn "postgresql://username:password@host:port/database"
# Optional: Run over HTTP transports
# Streamable HTTP (recommended for streaming tool outputs)
python postgres_server.py --transport streamable-http --host 0.0.0.0 --port 8000
# SSE transport (server-sent events) mounted at /sse and /messages/
python postgres_server.py --transport sse --host 0.0.0.0 --port 8000 --mount /mcp
```
2. The server provides the following tools:
- `query`: Execute SQL queries against the database
- `list_schemas`: List all available schemas
- `list_tables`: List all tables in a specific schema
- `describe_table`: Get detailed information about a table's structure
- `get_foreign_keys`: Get foreign key relationships for a table
- `find_relationships`: Discover both explicit and implied relationships for a table
- `db_identity`: Show current db/user/host/port, search_path, and version
Typed (preferred):
- `run_query(input)`: Execute with typed input (`sql`, `parameters`, `row_limit`, `format: 'markdown'|'json'`).
- `run_query_json(input)`: Execute and return JSON-serializable rows.
- `list_schemas_json(input)`: List schemas with filters (`include_system`, `include_temp`, `require_usage`, `row_limit`).
- `list_schemas_json_page(input)`: Paginated listing with filters and `name_like` pattern.
- `list_tables_json(input)`: List tables within a schema with filters (name pattern, case sensitivity, table_types, row_limit).
- `list_tables_json_page(input)`: Paginated tables listing with filters.
Examples:
```json
// run_query (markdown)
{
"sql": "SELECT * FROM information_schema.tables WHERE table_schema = %s",
"parameters": ["public"],
"row_limit": 50,
"format": "markdown"
}
// run_query_json
{
"sql": "SELECT now() as ts",
"row_limit": 1
}
```
Inspect current connection identity:
```json
// db_identity (no input)
{}
```
List schemas (JSON) with filters:
```json
{
"include_system": false,
"include_temp": false,
"require_usage": true,
"row_limit": 10000
}
```
Paginated list with pattern filter:
```json
{
"include_system": false,
"include_temp": false,
"require_usage": true,
"page_size": 200,
"cursor": null,
"name_like": "sales_*",
"case_sensitive": false
}
```
Response shape:
```json
{
"items": [ { "schema_name": "sales_eu", "owner": "...", "is_system": false, "is_temporary": false, "has_usage": true } ],
"next_cursor": "...base64..." // null when no more pages
}
```
List tables with filters (JSON):
```json
{
"db_schema": "public",
"name_like": "orders_*",
"case_sensitive": false,
"table_types": ["BASE TABLE", "VIEW"],
"row_limit": 1000
}
```
Paginated tables listing:
```json
{
"db_schema": "public",
"page_size": 200,
"cursor": null,
"name_like": "orders_%"
}
```
Resources (if supported by client):
- `table://{schema}/{table}` for reading table rows. Fallback tools are available:
- `list_table_resources(schema)` → `table://...` URIs
- `read_table_resource(schema, table, row_limit)` → rows JSON
Prompts (registered when supported; also exposed as tools):
- `write_safe_select` / `prompt_write_safe_select_tool`
- `explain_plan_tips` / `prompt_explain_plan_tips_tool`
### Running with Docker
Build the image:
```bash
docker build -t mcp-postgres .
```
Run the container without a database connection (the server stays inspectable):
```bash
docker run -p 8000:8000 mcp-postgres
```
Run with a live PostgreSQL database by supplying `POSTGRES_CONNECTION_STRING`:
```bash
docker run \
-e POSTGRES_CONNECTION_STRING="postgresql://username:password@host:5432/database" \
-p 8000:8000 \
mcp-postgres
```
*If the environment variable is omitted, the server boots normally and all database‑backed tools return a friendly “connection string is not set” message until you provide it.*
### Configuration with mcp.json
To integrate this server with MCP-compatible tools (like Cursor), add it to your `~/.cursor/mcp.json`:
```json
{
"servers": {
"postgres": {
"command": "/path/to/venv/bin/python",
"args": [
"/path/to/postgres_server.py"
],
"env": {
"POSTGRES_CONNECTION_STRING": "postgresql://username:password@host:5432/database?ssl=true"
}
}
}
}
```
### Transport Environment Variables
- `MCP_TRANSPORT=stdio|sse|streamable-http` (default: `stdio`)
- `MCP_HOST=0.0.0.0` and `MCP_PORT=8000` for SSE/HTTP transports
- `MCP_SSE_MOUNT=/mcp` optional SSE mount path
*If `POSTGRES_CONNECTION_STRING` is omitted, the server still starts and is fully inspectable; database‑backed tools will simply return an informative error until the variable is provided.*
Replace:
- `/path/to/venv` with your virtual environment path
- `/path/to/postgres_server.py` with the absolute path to the server script
### HTTP Client Integration
Run the server with Streamable HTTP:
```bash
python postgres_server.py --transport streamable-http --host 0.0.0.0 --port 8000
# or with Docker
docker run -p 8000:8000 mcp-postgres \
python postgres_server.py --transport streamable-http --host 0.0.0.0 --port 8000
```
Basic reachability check (expect non-200 since MCP expects a handshake):
```bash
curl -i http://localhost:8000/mcp
# A 404/405/422 indicates the server is reachable; clients must speak MCP.
```
Example MCP client config (conceptual) pointing at the Streamable HTTP endpoint:
```json
{
"servers": {
"postgres": {
"transport": "streamable-http",
"url": "http://localhost:8000/mcp"
}
}
}
```
For SSE instead of Streamable HTTP:
```bash
python postgres_server.py --transport sse --host 0.0.0.0 --port 8000 --mount /mcp
curl -N http://localhost:8000/sse # Connects to the SSE endpoint
```
#### Python MCP Client Example (Streamable HTTP)
```python
import asyncio
from mcp.client import streamable_http
from mcp.client.session import ClientSession
async def main():
url = "http://localhost:8000/mcp"
async with streamable_http.streamablehttp_client(url) as (read, write, _get_session_id):
session = ClientSession(read, write)
init = await session.initialize()
print("protocol:", init.protocolVersion)
# List tools
tools = await session.list_tools()
print("tools:", [t.name for t in tools.tools])
# Call typed tool: run_query_json
result = await session.call_tool(
"run_query_json",
{"input": {"sql": "SELECT 1 AS n", "row_limit": 1}},
)
# Prefer structuredContent if provided; fallback to text content
if result.structuredContent is not None:
print("structured:", result.structuredContent)
else:
print("text blocks:", [getattr(b, "text", None) for b in result.content])
if __name__ == "__main__":
asyncio.run(main())
```
## Security
- Never expose sensitive database credentials in your code
- Use environment variables or secure configuration files for database connection strings
- Consider using connection pooling for better resource management
- Implement proper access controls and user authentication
### Environment options
- `POSTGRES_READONLY=true` to allow only SELECT/CTE/EXPLAIN/SHOW/VALUES
- `POSTGRES_STATEMENT_TIMEOUT_MS=15000` to cap statement runtime
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
### Development & Tests
- Create a venv and install runtime deps: `pip install -r requirements.txt`
- (Optional) install test deps: `pip install -r dev-requirements.txt`
- Run tests: `pytest -q`
## Related Projects
- [MCP Specification](https://github.com/modelcontextprotocol/specification)
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
- [MCP Servers](https://github.com/modelcontextprotocol/servers)
## License
MIT License
Copyright (c) 2025 gldc
Permission is hereby granted, free of charge, to any person obtaining a copy
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copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
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