Folderr▌
by folderr-tech
Connect with Folderr to manage and interact with Folderr Assistants via API for seamless task automation and communicati
Integrates with Folderr's API to enable management and communication with Folderr Assistants, facilitating tasks like listing assistants and sending questions.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Automating interactions with Folderr AI assistants
- / Integrating Folderr workflows into other applications
- / Managing multiple assistants programmatically
capabilities
- / List available Folderr assistants
- / Send questions to specific assistants
- / Execute automated workflows
- / Authenticate with email/password or API token
- / Get workflow input requirements
- / List available workflows
what it does
Connects to Folderr's API to interact with AI assistants and execute workflows. Lets you list, query, and manage your Folderr assistants programmatically.
about
Folderr is an official MCP server published by folderr-tech that provides AI assistants with tools and capabilities via the Model Context Protocol. Connect with Folderr to manage and interact with Folderr Assistants via API for seamless task automation and communicati It is categorized under ai ml. This server exposes 7 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install Folderr 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
MIT
Folderr is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Folderr MCP Server
A Model Context Protocol (MCP) server that provides tools to interact with Folderr's API, specifically for managing and communicating with Folderr Assistants.
Installation
Add to your MCP Settings
{
"mcpServers": {
"folderr": {
"command": "npx",
"args": ["-y", "@folderr/folderr-mcp-server"]
}
}
}
Features
The server provides the following tools:
Authentication
Two methods of authentication are supported:
-
Login with Email/Password
{ "name": "login", "arguments": { "email": "[email protected]", "password": "your-password" } } -
API Token Authentication
{ "name": "set_api_token", "arguments": { "token": "your-api-token" } }API tokens can be generated from the Folderr developers section. This method is recommended for automated or long-running processes.
Assistant Management
-
List Assistants
{ "name": "list_assistants", "arguments": {} }Returns a list of all available assistants for the authenticated user.
-
Ask Assistant
{ "name": "ask_assistant", "arguments": { "assistant_id": "assistant-id", "question": "Your question here" } }Send a question to a specific assistant and receive their response.
Configuration
The server stores its configuration in a config.json file, which includes:
- Base URL for the Folderr API
- Authentication token (from login or API key)
Error Handling
The server provides detailed error messages for common scenarios:
- Authentication failures
- Invalid requests
- API errors
- Network issues
Development
To build the server:
npm install
npm run build
Usage in MCP Settings
Add the following to your MCP settings configuration:
{
"mcpServers": {
"folderr": {
"command": "node",
"args": ["/path/to/folderr-server/build/index.js"]
}
}
}
Authentication Flow
- Either:
- Use the
logintool with email and password - Use the
set_api_tokentool with an API token from Folderr's developers section
- Use the
- The authentication token is automatically saved and used for subsequent requests
- All assistant-related tools require authentication before use
Error Messages
Common error messages and their meanings:
- "Not logged in": No authentication token is set
- "Login failed": Invalid credentials
- "Failed to list assistants": Error retrieving assistant list
- "Failed to ask assistant": Error sending question to assistant
FAQ
- What is the Folderr MCP server?
- Folderr is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Folderr?
- This profile displays 51 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★51 reviews- ★★★★★Pratham Ware· Dec 24, 2024
Strong directory entry: Folderr surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Yuki Sharma· Dec 24, 2024
Folderr has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Dhruvi Jain· Dec 16, 2024
We evaluated Folderr against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Mei Haddad· Dec 16, 2024
According to our notes, Folderr benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Zara Iyer· Nov 19, 2024
Strong directory entry: Folderr surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dev Verma· Nov 15, 2024
Folderr is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★James Zhang· Nov 15, 2024
I recommend Folderr for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· Nov 7, 2024
Useful MCP listing: Folderr is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Zara Jackson· Nov 7, 2024
We wired Folderr into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Ganesh Mohane· Oct 26, 2024
Folderr reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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