#planning

A minimal but better Canvas LMS MCP (Machine Conversation Protocol) server for easy access to education data through your Canvas LMS instance. This server provides a bridge between AI systems (like Cursor) and Canvas Learning Management System.

This project provides a modular Python wrapper for the SNCF API, with an MCP server interface that integrates seamlessly with Claude Desktop for intelligent journey planning and train information retrieval across France.

The MCP server for interacting with Blockchain, Swaps, Strategic Planning and more.

An MCP server allowing LLMs to interact with Ansys/AGI STK - Digital Mission Engineering Software

This MCP server gives LLM/AI/Agents the ability to provide accurate navigation information. There are three main application areas (all can be combined): time & place, road-trips, and general aviation.

A comprehensive Model Context Protocol (MCP) server that integrates Sunsama's daily planning and task management capabilities into AI assistants. Provides full CRUD operations for tasks, enabling automated workflow management and productivity optimization through the Sunsama API. Key features: Task Management: Create, read, update, and delete tasks with notes, time estimates, due dates, and stream assignments Daily Planning: View tasks by specific day with completion filtering and access backlog tasks Task Operations: Mark tasks complete with custom timestamps, reschedule tasks, or move items to backlog User & Organization: Access user profile, timezone, group details, and stream/channel management Flexible Transport: Supports both stdio and HTTP stream MCP transports Easy Setup: Use directly with npx mcp-sunsama or install locally for development Perfect for building AI-powered productivity workflows, automated task management, and custom interfaces that sync with your Sunsama workspace.

The pre.dev MCP Server is an MCP endpoint that plugs into AI coding agents and IDEs (Cursor, Claude Code, VS Code, Windsurf) to replace lightweight planning modes with production-ready specification generation. It outputs enterprise-grade architecture (APIs, DB schemas, component hierarchy) and step-by-step implementation roadmaps via two tools—fast_spec for quick MVPs and deep_spec for comprehensive systems—so agents can reliably plan and build complex, multi-file projects.

Career and workforce intelligence built on a deep HR ontology — skill taxonomies, role definitions and responsibilities, compensation and incentive structures, learning and development pathways, sourcing strategies, and role/skill evolution mapping. This structured foundation, combined with a RAG knowledge base curated from 50+ premium sources (HBR, McKinsey, BCG, Gartner, Forrester) and updated 3x daily with live web research, powers 6 guided skills and 42 MCP tools for two audiences: working professionals getting personalized career intelligence (CV optimization, salary benchmarking, career strategy), and HR/TA teams running structured talent evaluation, candidate shortlisting, compensation analysis, and consulting-grade workforce research reports. Example Use Cases (for HR/TA teams): 1. Custom Evaluation Models — Train CareerProof on your organization's existing assessment rubrics, scorecards, and evaluation criteria to build custom eval models that evaluate candidates through your specific lens. Upload your competency frameworks and historical assessments, then run inference on new candidates — scored and ranked exactly how your team would, at scale. 2. Candidate Evaluation & Shortlisting — Set up a hiring context with company profile and job description, upload candidate CVs, then batch-rank them with GEM competency scoring and JD-FIT matching. Apply your custom eval models for organization-specific scoring, or deep-dive any candidate with a 360-degree evaluation including tailored interview questions derived from skill taxonomy analysis. 3. Workforce Research Reports — Generate consulting-grade PDF reports across 16 types (salary benchmarking, skills gap analysis, org design, DEI assessment, succession planning, sourcing strategy, and more). Each report is grounded in real-time market data from premium sources and structured around HR ontology — role definitions, compensation structures, L&D pathways, and skill evolution mapping. 4. Compensation & Incentive Benchmarking — Get market-calibrated salary and total compensation intelligence for any role, location, and industry. Analysis is structured around compensation and incentive frameworks from the HR ontology, enriched with live web research and curated knowledge base data covering base salary, equity, bonuses, and benefits. Example Use Cases (for the working professional or career coach): 1. Career Intelligence Chat (Hyper-Personalized) — Ask career strategy questions and get hyper-personalized responses that fuse your CV context with deep insights from the career and workforce RAG knowledge base. Salary benchmarks calibrated to your function and location, industry disruption analysis mapped to your skill profile, and career pivot recommendations grounded in role evolution data — not surface-level answers, but intelligence drawn from the same sources that inform executive strategy. 2. CV Optimization (Hyper-Personalized) — Upload your CV and receive a hyper-personalized positioning pipeline that combines your actual experience with deep insights from our career and workforce RAG knowledge base. Market analysis calibrated to your industry and seniority, career opportunity identification grounded in role/skill evolution data, and targeted edits with trade-off analysis — not generic advice, but intelligence shaped by 50+ premium research sources and your unique career trajectory.