
MCPFinder
Lets AI assistants discover and install new MCP server capabilities on demand from a central registry. Acts like an app store for AI tools with zero-friction setup.
Enables AI assistants to discover and integrate new capabilities by providing tools for searching, retrieving details, and configuring MCP servers across various applications.
What it does
- Search MCP server registry for new tools and capabilities
- Retrieve detailed installation info for specific servers
- Add MCP server configurations to client applications
- Remove MCP server configurations from clients
- Monitor real-time registry events and updates
Best for
About MCPFinder
MCPFinder is a community-built MCP server published by mcpfinder that provides AI assistants with tools and capabilities via the Model Context Protocol. MCPFinder empowers your AI powered writing assistant to discover and configure new capabilities across applications seam It is categorized under developer tools. This server exposes 5 tools that AI clients can invoke during conversations and coding sessions.
How to install
You can install MCPFinder in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
MCPFinder is released under the AGPL-3.0 license.
Tools (5)
This is the primary tool for discovering and accessing new tools, methods, features, or capabilities. **If a user asks for a tool, method, feature, or capability that you do not currently have, you MUST use this tool to search for it in the MCP Finder registry.** For example, if a user asks for a browser tool, OCR, filesystem access, PDF parsing, or any other capability not present in your current toolset, use this tool to search by relevant keywords. This is the required first step to expand your available tools.
Retrieves detailed information about a specific MCP server/tool from the registry, including its manifest and installation details. Use this after finding a tool with search_mcp_servers to get more information, or directly use add_mcp_server_config to install it.
Enables capabilities (e.g., tools, features) from a specific MCP server/tool. Add or update its configuration in the client application (e.g., Cursor, Claude Desktop, Windsurf, Claude Code, Codex) using server_id obtained from search_mcp_servers results. Provide EITHER client_type (see available options) OR config_file_path to specify the target config file.
Removes the configuration for a specific MCP server/tool from the client application (e.g., Cursor, Claude Desktop, Windsurf, Claude Code, Codex). Provide EITHER client_type (see available options) OR config_file_path to specify the target config file.
Monitor real-time events from the MCPfinder registry including new tool registrations, updates, and status changes. Returns a summary of events received during the monitoring period.
MCPfinder 🔧🤖 (@mcpfinder/server)
Meet the simplest way to supercharge your coding and AI agents with MCP — an "API for AI." MCPfinder enables language models to search for and install new capabilities on demand through client applications that support the MCP protocol. No coding or manual setup required.
App Store for Agents
Users can request tools the AI doesn't have yet, or the AI can autonomously expand its own capabilities by discovering relevant MCP servers. From code generators and data analyzers to specialized knowledge tools, MCPfinder acts like a map and toolbox for AI — transforming static models into evolving, capability-hunting agents that grow more powerful with every interaction.
Plug-and-Play Tools for LLMs
This Node.js application implements an MCP (Model Context Protocol) server designed to run locally alongside MCP clients (like Cursor, Claude Desktop, Windsurf). It provides tools that allow AI within the client to interact with the MCP ecosystem.
Specifically, it enables the AI assistant to:
- Discover available MCP servers registered in the central MCPfinder Registry (via
search_mcp_servers). - Retrieve details about specific servers (via
get_mcp_server_details). - Manage the client application's local MCP server configuration file (add/update via
add_mcp_server_config, remove viaremove_mcp_server_config).
Note for AI Agents: This server is the primary interface for finding and enabling new tools and capabilities requested by the user if they aren't already available in current MCP toolset. Use search_mcp_servers first when asked by the user for a capability you don't possess.
⚡ Instant Availability
We curate only MCP servers that are instantly available to AI agents — no manual setup, no cloning repositories, no complex installation steps. Just one command or API call, and your AI gains new capabilities immediately.
Zero-Friction Setup
All servers work via npx (Node.js packages), uvx (Python packages), or direct HTTP/SSE endpoints
AI-Ready
Your AI can discover and install tools autonomously, no human intervention needed
Real-Time Enhancement
Capabilities expand on-demand, exactly when your conversation needs them
Quick Start
Option 1: Local Installation (stdio transport)
Run in your terminal the interactive setup tool to automatically update the MCP configuration file:
npx -y @mcpfinder/server install
This command guides you through selecting your client (Cursor, VS Code, Claude, etc.) and adds the necessary mcpfinder entry to the correct configuration file (e.g., ~/.cursor/mcp.json).
Option 2: Cloud Access (HTTP/SSE transport)
You can also access MCPfinder directly via HTTP/SSE transport without local installation:
# For Claude CLI users
claude mcp add --transport http mcpfinder https://mcpfinder.dev/mcp
# Direct HTTP endpoint
https://mcpfinder.dev/mcp
This provides the same tools (search_mcp_servers, get_mcp_server_details, etc.) but runs in the cloud without needing local Node.js or npm.
See "Running from source" and "Commands and Options" for more details if you are working directly with the source code.
Manual Configuration
To manually configure an MCP client, you need to create or modify its JSON configuration file to include an entry for mcpfinder.
Stdio Transport Configuration (Local)
Configuration File Structure:
{
"mcpServers": {
"mcpfinder": {
"command": "npx",
"args": [
"-y",
"@mcpfinder/server"
]
},
}
}
Note: For Visual Studio Code (settings.json), the top-level key for MCP configurations must be servers instead of mcpServers.
HTTP/SSE Transport Configuration (Cloud)
For clients that support HTTP/SSE transport:
{
"mcpServers": {
"mcpfinder": {
"url": "https://mcpfinder.dev/mcp",
"transport": "http"
}
}
}
Running from source
- Clone this repository, e.g.,
git clone https://github.com/mcpfinder/server - Run
node index.jsfor Stdio mode ornode index.js --httpfor HTTP mode.
Commands and Options
When running from source (node index.js), the script can be invoked in several ways:
Running the Server (Default Behavior):
If no command is specified, index.js starts the MCP server.
- Stdio Mode (default):
node index.js - HTTP Mode (local):
node index.js --http--port <number>: Specify the port for HTTP mode (default: 6181, orMCP_PORTenv var).--api-url <url>: Specify the MCPfinder Registry API URL used by the tools (default:https://mcpfinder.dev, orMCPFINDER_API_URLenv var).
Note: The HTTP mode runs locally on your machine. For cloud-based HTTP/SSE access, use the public endpoint at https://mcpfinder.dev/mcp instead.
Executing Commands:
-
install: Run the interactive setup to configure a client application.node index.js install -
register: For server publishers to register their MCP server package with the MCPFinder registry.node index.js register [package-name-or-url] [options] # or when installed globally: npx -y @mcpfinder/server register [package-name-or-url] [options]Register Command Options:
--headless: Run registration without interactive prompts (uses defaults)--use-uvx: Specify that the package should be run withuvxinstead ofnpx(for Python packages)--description <text>: Provide a description for the server--tags <tags>: Comma-separated list of tags (e.g., "database,api,search")--auth-token <token>: Authentication token for the registry--requires-api-key: Indicate that the server requires an API key--auth-type <type>: Type of authentication (default: "api-key")--key-name <name>: Name of the API key environment variable--auth-instructions <text>: Instructions for obtaining API keys--confirm <y/n>: Auto-confirm registration without manual approval--manual-capabilities <y/n>: Manually specify capabilities instead of auto-detection--has-tools <y/n>: Specify if the server provides tools--has-resources <y/n>: Specify if the server provides resources--has-prompts <y/n>: Specify if the server provides prompts
This command will:
- Accept npm package names (e.g.,
@username/my-mcp-server) or HTTP/SSE endpoints - Support both Node.js packages (
npx) and Python packages (uvx) - Automatically connect to your MCP server to verify it's valid
- Introspect available tools, resources, and prompts
- Generate a manifest with your server's capabilities
- Submit it to the MCPfinder registry
Getting Help:
--help: Display the help message detailing commands and options.node index.js --help
The server uses the following environment variables:
MCPFINDER_API_URL: The base URL for the MCPfinder Registry API. Defaults tohttps://mcpfinder.dev.MCP_PORT(HTTP Mode Only): The port number for the server to listen on. Defaults to6181.
Provided Tools
This MCP server exposes the following tools to the connected AI assistant (available via both stdio and HTTP/SSE transports):
1. search_mcp_servers
- Description: Searches the MCPfinder Registry for available MCP servers. This is the primary tool for discovering and accessing new tools, methods, features, or capabilities.
- Input Schema:
query(string, optional): Keywords to search for in tool name or description.tag(string, optional): Specific tag to filter by.
- Output: A list of matching server summaries (server_id, name, description, URL, tags). The typical next step is to use
get_mcp_server_detailsfor more info or directlyadd_mcp_server_configto install one.
⚠️ Note: The registry currently contains several hundred servers that can be run locally using npx (Node.js) or uvx (Python) in stdio mode without requiring environment variables for basic operation. Future updates will expand support to include a wider range of servers, including paid and commercial options that require environment keys.
2. get_mcp_server_details
- Description: Retrieves detailed information about a specific MCP server from the registry, including its full manifest and basic installation suggestions (command, environment variables). Use this after finding a server_id via
search_mcp_serversto get more information before potentially adding it. - Input Schema:
id(string, required): The unique MCPfinder's server_id obtained fromsearch_mcp_servers.
- Output: The detailed server manifest and installation hints. The next step is to use
add_mcp_server_configto install the server.
3. add_mcp_server_config
- Description: Adds or updates the configuration for a specific MCP server in the client application's local configuration file (e.g., Cursor's
~/.cursor/mcp.json). You must provide eitherclient_typeORconfig_file_path. - Input Schema:
server_id(string, required): A unique identifier for the server configuration entry (the MCPfinder ID obtained fromsearch_mcp_servers).client_type(string, optional): The type of client application (known types determined dynamically, examples:'cursor','claude','windsurf'). Mutually exclusive withconfig_file_path. Use this for standard client installations.config_file_path(string, optional): An absolute path or a path starting with~(home directory) to the target JSON configuration file (e.g.,/path/to/custom/mcp.jsonor~/custom/mcp.json). Mutually exclusive withclient_type. Use this for non-standard locations or custom clients.
README truncated. View full README on GitHub.
Alternatives
Related Skills
Browse all skillsUI design system toolkit for Senior UI Designer including design token generation, component documentation, responsive design calculations, and developer handoff tools. Use for creating design systems, maintaining visual consistency, and facilitating design-dev collaboration.
Guide for building TypeScript CLIs with Bun. Use when creating command-line tools, adding subcommands to existing CLIs, or building developer tooling. Covers argument parsing, subcommand patterns, output formatting, and distribution.
Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.
Master API documentation with OpenAPI 3.1, AI-powered tools, and modern developer experience practices. Create interactive docs, generate SDKs, and build comprehensive developer portals. Use PROACTIVELY for API documentation or developer portal creation.
Integrate Vercel AI SDK applications with You.com tools (web search, AI agent, content extraction). Use when developer mentions AI SDK, Vercel AI SDK, generateText, streamText, or You.com integration with AI SDK.
Use when building MCP servers or clients that connect AI systems with external tools and data sources. Invoke for MCP protocol compliance, TypeScript/Python SDKs, resource providers, tool functions.