AI-powered pipeline that analyzes user stories and PRDs for gaps, ambiguities, and missing acceptance criteria before coding, with MCP tools for requirement analysis and risk reporting.
A read-only MCP server for Anaplan that exposes 5 tools for business users to explore data through an AI assistant, with session caching and metadata gating.
It enables AI agents to parse user agent strings into structured data such as browser, OS, device type, engine, and bot status, with pay-per-call micropayments via x402 and no API key or signup required.
Enables interaction with Discord using personal user tokens instead of bot applications, allowing for seamless message management and server exploration. It provides tools for reading history, sending messages, and searching across channels and DMs directly through MCP-compatible clients.
An MCP App that enables AI agents to ask users multiple questions with tab-based navigation, multiple-choice options, multi-select support, and custom text input, all rendered inline in the conversation.
Enables Codex to clarify requirements via native MCP elicitation controls, supporting single/multiple-choice and free-text questions with recommended answers and a discuss-first option.
Provides an interactive checklist tool that allows AI agents to present step-by-step instructions to users through an automatically opened terminal UI. It enables agents to guide users through manual tasks and wait for completion, skipping, or feedback before proceeding.
Enables existing Express routers to be exposed as MCP tools, dispatching tool calls through the Express middleware stack in-process without a network hop.
Enables converting text to speech audio in 20+ languages, returning base64-encoded MP3 output via Google TTS with x402 micropayment-based pay-per-call access.
Enables converting Markdown text into clean HTML for headings, lists, code blocks, tables, links, and images, with optional full-document wrapping via x402 micropayments.
Enables MCP-capable agents to retrieve relevant slices of a user-story knowledge graph (stored in Supabase + pgvector) using three retrieval verbs: find_related, find_crossover, and query_stories.