"A scheduling engine powered by AI" matching MCP servers:

Enables AI assistants to recommend jobs, parse candidate profiles, compute semantic skill match scores, and filter opportunities by location through standardized MCP tools.

Enables LLM clients to access scheduling data, KPIs, routes, and trigger optimization algorithms via MCP.

An MCP-native CRM backend for AI agents, enabling customer, opportunity, note, follow-up, and pipeline health management through 15 MCP tools.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Exposes self-owned AI agent plugins as tools to Claude Code via a stdio MCP server, allowing Claude to call system info, calculator, or scribe transcriber directly.

Enables grounded, dated research from a curated AI-knowledge repository, offering free BM25 retrieval and a research loop that combines the knowledge base, curated sources, and the open web with tiered citations.

Eyes for AI coding agents: deterministic MCP server that tells the LLM which CSS rule wins, in which file, on which line — and why. Cascade verdicts, blast radius, interaction timelines, pixel-perfect audits.

Local MCP tools to create, preview, inspect, verify, and compile PDF documents from HTML/CSS and structured data with Fullbleed. Python/Rust engine, MIT licensed, with document paths confined to a chosen workspace.

attendance-engine-mcp is a Model Context Protocol server that gives AI agents deterministic, fixture-backed tools for workforce attendance and wage-and-hour compliance. Built on @attendance-engine/core — a pure-function, zero-deps, 100%-covered TypeScript engine — it lets Claude, Cursor, Windsurf, or any MCP host correctly answer the questions HR/payroll teams actually ask: did this person clock i

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

MCP server (stdio, official Python SDK) exposing a single tool: quantity_execute. Performs dimensional calculation and unit conversion with evidence. Solves silent unit errors in LLM agent chains: 20 m/s × 3 h returns 216 km.

Enables programming agents to capture errors and conversation signals, reflect on root causes, consolidate reusable skills, and retrieve relevant context for future tasks, providing a self-learning memory loop.

Provides AI agents with 200 deterministic micro-services covering code auditing, 3D/robotics, media processing, cryptography, blockchain settlement, AI safety, networking, and math, with automatic x402 USDC micropayments.

MCP server giving AI agents 86 tools for browser automation via Chrome DevTools Protocol, including navigation, click/fill ladders, network inspection, per-site cracks, and optional marketplace for site-specific knowledge packs.

Graph RAG based context management engine for coding agents

Persistent memory for AI coding assistants.

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

An MCP server that brings difficulty-adaptive, multi-path reasoning to Claude Code. It implements the Actor-Critic-Planner-Reflexion (ACPR) pipeline for deep research synthesis.

Headless InDesign document composition via MCP: parse IDML templates, convert content to ICML stories, and compose complete newspaper IDML files conversationally.

A Stockfish-powered chess engine exposed as an MCP server using FastMCP. Calculates best moves, validates moves, and provides game status via MCP tools over SSE or stdio.