"How to allow an LLM to ask questions to a user" matching MCP servers:

Enables AI agents to directly control Figma Desktop via MCP, supporting UI creation, editing, prototyping, and variable management with over 60 tools.

An MCP server that integrates AI agents with Figma, enabling reading and programmatic modification of designs.

Enables AI assistants to read and modify Figma designs programmatically through the Model Context Protocol.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

Provides AI assistants with direct access to Jamf official documentation, enabling them to answer Jamf-related questions by searching, retrieving articles, and browsing product documentation.

An MCP server that lets LLM agents operate the IDnow Trust Platform sandbox to list verification flows, create sessions, check sessions, and get session details.

Enables AI assistants to answer customer questions from a shop's own product catalog, FAQs, and policy documents, with read-only access and cited answers.

A simple MCP server that provides a basic greeting tool and serves as a starter template for AWS Lambda deployment. Demonstrates how to build and deploy MCP servers with both local development and cloud deployment capabilities.

An MCP server that enables AI assistants to perform web searches on Perplexity.ai using browser automation instead of an official API. It supports persistent authenticated sessions and returns search results along with cited sources directly to the client.

An enhanced MCP server that enables users to interact with NotebookLM while ensuring strict source fidelity and citation accuracy through client-side prompt structuring. It guides Claude to transform user queries into structured prompts that prevent the addition of external knowledge during document analysis.

Enables encoding JSON into compact TOON format and decoding TOON back to JSON, reducing token usage for LLM prompts.

AI job search and job application tracker. Search live jobs by title, location, salary, date posted, and remote or on-site, and see each one checked on real data: pay against market rates, ghost-job signals, visa sponsorship history, and how well it matches your resume. Check how hiring software reads your resume, track applications, set follow-up reminders, and get email alerts.

A read-only MCP server that provides family-context tools for the A2Me platform, enabling AI assistants to answer family-related questions and help write messages by securely accessing family member profiles, upcoming events, and recent activity.

Enables LLM clients to generate standardized test cases, perform quality control with lint scoring, convert to Xray/Jira format, and compose test suites (Smoke/Regression/E2E) with coverage analysis.

A CLI-based Model Context Protocol server that exposes PubNub SDK documentation and Functions resources to LLM-powered tools like Cursor IDE, enabling users to fetch documentation and interact with PubNub channels via natural language prompts.

Enables AI assistants to search, list, and retrieve documentation from the Slice.js official GitHub repository. It provides full-text search capabilities and can deliver individual doc pages or a complete documentation bundle for comprehensive LLM context.

A Model Context Protocol server that allows AI assistants and applications to access IQ.wiki data, enabling retrieval of specific wikis, user-created wikis, user-edited wikis, and detailed wiki activities.

MCP server for multi-tenant DuckDB management with R2/S3 cloud storage, enabling AI agents to manage per-user databases with automatic cloud persistence.

Enables AI assistants to answer tax compliance questions (VAT, sales tax, GST) and validate EU VAT numbers in real time via the VIES registry.

An MCP server providing full integration with the Lunch Money API to manage financial data including transactions, budgets, assets, and categories. It enables AI assistants to perform CRUD operations on financial records through a standardized HTTP interface.