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# CarAPI MCP Server Remote MCP server that gives AI agents a single interface to **CarAPI**'s vehicle-data endpoints — VIN decoding, license-plate lookup, stolen-vehicle checks, technical-inspection history, odometer history, market valuation, listings search, and more. Hosted at **https://mcp.carapi.dev/mcp** (Streamable HTTP). Stateless, horizontally scalable, no session storage.

Regulatory intelligence API — look up permits, licenses, and fees for food service businesses in Austin, SF, and NYC.

MCP server with 22 tools for marketplace search, license verification, childcare costs, estate sales, FCC IDs, GSA auctions, and more across 15+ platforms

Decode VINs, look up specs, history, recalls, market value, and OBD codes. Recognize license plates and VINs from images. Access comprehensive vehicle data by year, make, and model to power automotive workflows.

Research-Quest A comprehensive desktop extension implementing the Research-Quest framework for systematic scientific reasoning through an 8-stage graph-based methodology. Overview The Research-Quest desktop extension provides researchers with a powerful tool for conducting systematic scientific analysis through a structured, graph-based approach. This extension implements the complete Research-Quest framework as defined in the Research-Quest.md specification, enabling: - Systematic Research Methodology: 8-stage process from initialization to reflection - Multi-dimensional Confidence Tracking: Bayesian belief updates with statistical rigor - Interdisciplinary Research Support: Bridge nodes connecting different domains - Causal Inference Capabilities: Pearl's do-calculus and counterfactual reasoning - Temporal Pattern Analysis: Dynamic relationship modeling - Bias Detection & Mitigation: Systematic bias identification and correction - Impact Assessment: Research significance and utility estimation - Collaborative Research Features: Multi-researcher attribution and consensus building Architecture 8-Stage Research-Quest Framework 1. Initialization: Create root node with task understanding 2. Decomposition: Break down research task into fundamental dimensions 3. Hypothesis/Planning: Generate competing hypotheses with detailed metadata 4. Evidence Integration: Bayesian confidence updates with typed relationships 5. Pruning/Merging: Graph refinement based on confidence and impact 6. Subgraph Extraction: Focus on high-value research pathways 7. Composition: Generate structured research narratives 8. Reflection: Comprehensive quality audit and validation

QuerySculptor is a GraphQL query builder designed for AI agents that provides 26 structured tools via the Model Context Protocol (MCP). Instead of generating complex GraphQL queries from scratch, AI agents can build queries step-by-step with real-time validation, schema introspection, and error prevention. It works with Claude, Cursor, and other MCP clients, using Redis for session management and featuring a live demo with the Pokemon API. The tool transforms the traditional error-prone approach of raw GraphQL generation into a guided, conversational workflow perfect for AI assistants and chatbots.

MCP Server for Thai plate prophecy. This project is designed to help customers predict or "prophecy" potential license plate numbers. By leveraging algorithms and user input, it provides insights and suggestions for plate numbers that may hold significance or align with user preferences. โครงการนี้ถูกออกแบบมาเพื่อช่วยให้ผู้ใช้งานสามารถทำนายหรือ "พยากรณ์" หมายเลขทะเบียนรถที่เป็นไปได้ โดยใช้การประมวลผลผ่านอัลกอริทึมและข้อมูลที่ผู้ใช้งานให้มา เพื่อให้คำแนะนำและข้อมูลเชิงลึกเกี่ยวกับหมายเลขทะเบียนที่อาจมีความหมายหรือสอดคล้องกับความชอบของผู้ใช้งาน