pg-mcp
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<h1 id="pg-mcp-client">pg-mcp-client</h1>
<h2 id="installation">Installation</h2>
<h3 id="prerequisites">Prerequisites</h3>
<ul>
<li>Python 3.13+</li>
<li>A running <a href="../server/">pg-mcp-server</a></li>
<li>API key from one of the supported AI providers</li>
</ul>
<h3 id="using-docker">Using Docker</h3>
<div class="highlight"><pre><span></span><code><span class="c1"># Clone the repository</span>
git<span class="w"> </span>clone<span class="w"> </span>https://github.com/stuzero/pg-mcp-client.git
<span class="nb">cd</span><span class="w"> </span>pg-mcp-client
<span class="c1"># Create a .env file with your application secret</span>
<span class="nb">echo</span><span class="w"> </span><span class="s2">"APPLICATION_SECRET=your_secure_random_string"</span><span class="w"> </span>><span class="w"> </span>.env
<span class="c1"># Build and run with Docker</span>
docker-compose<span class="w"> </span>up<span class="w"> </span>-d
</code></pre></div>
<h3 id="manual-installation">Manual Installation</h3>
<div class="highlight"><pre><span></span><code><span class="c1"># Clone the repository</span>
git<span class="w"> </span>clone<span class="w"> </span>https://github.com/stuzero/pg-mcp-client.git
<span class="nb">cd</span><span class="w"> </span>pg-mcp-client
<span class="c1"># Install dependencies and create a virtual environment ( .venv )</span>
uv<span class="w"> </span>sync
<span class="c1"># Activate the virtual environment</span>
<span class="nb">source</span><span class="w"> </span>.venv/bin/activate<span class="w"> </span><span class="c1"># On Windows: .venv\Scripts\activate</span>
<span class="c1"># Create a .env file with your application secret</span>
<span class="nb">echo</span><span class="w"> </span><span class="s2">"APPLICATION_SECRET=your_secure_random_string"</span><span class="w"> </span>><span class="w"> </span>.env
<span class="c1"># Run the application</span>
python<span class="w"> </span>-m<span class="w"> </span>client.app
</code></pre></div>
<h2 id="configuration">Configuration</h2>
<ol>
<li>Access the web interface at http://localhost:8080</li>
<li>Navigate to the Settings page and configure:</li>
<li><strong>LLM Provider</strong>: Select from Anthropic (Claude), Gemini, or OpenAI</li>
<li><strong>LLM API Key</strong>: Your API key for the selected provider</li>
<li><strong>PG-MCP Server URL</strong>: The URL of your PG-MCP server's SSE endpoint (e.g., http://localhost:8000/sse)</li>
<li><strong>Database URL</strong>: PostgreSQL connection string for the target database</li>
</ol>
<h2 id="usage">Usage</h2>
<ol>
<li>After configuring settings, go to the Query page</li>
<li>Enter your question in natural language (e.g., "Show me the top 10 customers by revenue")</li>
<li>Click "Execute Query"</li>
<li>View the generated SQL and query results</li>
</ol>
<h2 id="example-queries">Example Queries</h2>
<p>Try these example queries to get started:</p>
<ul>
<li>"List all tables in the database"</li>
<li>"Show me the first 5 rows from the customers table"</li>
<li>"What were the total sales by month in 2023?"</li>
<li>"Find customers who haven't placed an order in the last 3 months"</li>
<li>"What product categories have the highest profit margin?"</li>
</ul>
<h2 id="supported-llm-models">Supported LLM Models</h2>
<ul>
<li><strong>Anthropic</strong>: Claude 3.7 Sonnet (recommended for best results)</li>
<li><strong>Google</strong>: Gemini 2.0 Flash</li>
<li><strong>OpenAI</strong>: GPT-4o Mini <em>(untested)</em></li>
</ul>
<p>For more details, visit the <a href="https://github.com/stuzero/pg-mcp-client">pg-mcp-client GitHub repository</a>.</p>
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