A local, read-only MCP server (plus CLI) over your own stock/ETF transaction log. A deterministic core computes drawdown-first risk with bootstrap confidence intervals and validates targets with walk-forward verdicts. The assistant narrates, but the number fence makes it structurally impossible for the model to produce a figure.
A cross-AI-agent local task context sharing MCP server that allows clients like Codex, Claude Code, Hermes, and VS Code to read and write the same task state, enabling task tracking and context distillation.
Provides task and project management tools for AI agents, including todo list management, prioritization frameworks like Eisenhower Matrix and RICE scoring, time estimation using PERT, daily standup generation, and sprint burndown calculation.
Enables comprehensive task management with groups, custom statuses, and task relationships, designed for AI assistants to manage tasks during conversations.
Enables long-running coding or documentation tasks to save incremental checkpoints, resume after interruptions, restore any previous step, and hand off work across sessions or machines using only the Python standard library.
Enables agents to select the provably optimal set of candidate actions under a budget, with optional conflicts and prerequisites, and to re-plan to the single best next action after each outcome. Also validates any proposed selection for feasibility.
Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.
Exposes employee leave-management operations (add employees, check balance, apply/cancel leave, view history) as MCP tools for AI clients like Claude Desktop to call directly.
Provides persistent project memory, task lists, time tracking, and shared rules and learned preferences so coding agents can recall decisions and conventions across sessions and coordinate multi-step work.
MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.
An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.
Enables AI agents to autonomously manage and improve execution processes for repetitive task types by storing reusable task contexts with associated artifacts (practices, rules, prompts, learnings) and providing full-text search across historical best practices.
Connects AI assistants to your n8n instance to manage workflows, executions, credentials, and user settings through 31 specialized tools. It enables users to perform n8n operations like workflow creation, activation, and execution history retrieval via natural language.
Read-only MySQL MCP server that lets AI agents list tables, describe schemas, and run SELECT/SHOW/EXPLAIN queries with a row cap, bound to a single database for safety.
Integrates Azure Cost Management APIs via MCP, enabling cost queries, forecasts, budgets, and Advisor recommendations through natural language in compatible clients.
An MCP server that provides live status updates for the Eco via Sirens game server, displaying meteor countdowns, player statistics, and world information directly within Claude Desktop. It also serves as a minimal reference implementation for building MCP Apps in Python without complex tooling.
MCP server for looking up apps, reviews, ratings, charts, in-app purchases, privacy details, and version history on Apple's App Store. It requires no API key.
An orchestrator MCP server that takes a plain-language app description and guides agents through a full workflow from intake to deployment, including design, audit, and maintenance.
Manage Job using MCP: Manage Job, Candidates, Resumes, Salaries all within this one MCP tools
It can solve problems like:
You have 50 resumes to screen. Your AI assistant can reason about candidates, but it can't:
Read PDFs/DOCX — The AI can't open binary files
Extract structured data — Copy-pasting loses formatting, metrics, and context
Compare at scale — No consistent scoring across candida