Feature Request Collection MCP Server

MCP server with Supabase integration

Local serverstdio

What is the Feature Request Collection MCP MCP server?

Feature Request Collection MCP MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. MCP server with Supabase integration.

What you get

This is a Model Context Protocol (MCP) server that connects to Supabase and allows you to query the feature_suggestions table.

Setting it up

Installation goes through your MCP client rather than a global install: point it at tsx on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.

What the assistant can call

Once Feature Request Collection MCP is connected, these are the calls the assistant has available:

  • Cursor — 1. Open Cursor and navigate to Cursor Settings. 2. Under the Features tab, tap + Add new MCP server under the MCP Servers section. 3
  • query_feature_suggestions — Query the feature_suggestions table in your Supabase database

Configuration and credentials

You will need 2 environment variables: SUPABASE_URL, SUPABASE_ANON_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.

  • Node.js (v16 or higher) - npm - Supabase project with credentials

Before you rely on it

  • It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
  • Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
  • MCP clients confirm each tool call by default. Leave that on until you have watched what the feature request collection mcp mcp server does with a few real requests.

Choosing this one

This sits in the database access group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Feature Request Collection MCP's toolset — Cursor, query_feature_suggestions — is a fair guide to whether it matches your workflow. It is maintained by adiletD; worth a glance at recent repository activity before you build anything load-bearing on it.

This entry was verified against Feature Request Collection MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.

Available tools

ToolWhat it does
Cursor1. Open Cursor and navigate to **Cursor Settings**. 2. Under the **Features** tab, tap **+ Add new MCP server** under the **MCP Servers** section. 3. Enter the following details: - **Name**: Supabase - **Type**: command
query_feature_suggestionsQuery the feature_suggestions table in your Supabase database.

How to install the Feature Request Collection MCP MCP server

{
     "mcpServers": {
       "supabase": {
         "command": "npx",
         "args": ["tsx", "/path/to/mcp-server.ts"]
       }
     }
   }

Configuration as documented by the project. Restart the client after saving.

Configuration

  • Node.js (v16 or higher) - npm - Supabase project with credentials
VariableDescriptionRequired
SUPABASE_URLEndpoint or connection string the server talks to.Yes
SUPABASE_ANON_KEYCredential the server authenticates with.Yes

Example prompts to try

  • Use Feature Request Collection MCP to Cursor.
  • Use Feature Request Collection MCP to query feature suggestions.

Frequently asked questions

It connects Feature Request Collection MCP to MCP-compatible AI assistants such as Claude and Cursor, exposing 2 tools (Cursor, query_feature_suggestions) that the assistant can call on your behalf. Instead of copying data back and forth by hand, the assistant works with Feature Request Collection MCP directly.