"A server offering MCP prompts as primitives for task execution" matching MCP servers:

Enables AI agents to interact with the Execute.run bot API for managing Shell balances, transferring funds, and executing LLM requests. It provides tools for identity verification, transaction tracking, and performing compute tasks through the Execute.run platform.

A Model Context Protocol server for managing prompt templates as markdown files with YAML frontmatter, allowing users and LLMs to easily add, retrieve, and manage prompts.

Enables searching for high-quality AI art prompts from Banana Prompts by tags or keyword queries, with direct links to full prompts and images.

Verifies AI agent completion claims by checking that claimed output files exist and are fresh, acting as a lightweight physical telemetry gate.

Enables creation, management, and templating of prompts through a simplified SOLID architecture, allowing users to organize prompts by category and fill in templates at runtime.

An MCP server that helps AI coordinate sequential tool calls and maintain a comprehensive journal of execution workflows, decisions, and actions.

An enhanced MCP server that fetches prompts from GitHub repositories with intelligent discovery, composition, and management features.

Enables LLMs to inspect and test other MCP servers by listing and calling their tools, reading resources, and getting prompts. Supports stdio, SSE, and HTTP transports for both local and remote MCP server inspection.

Elevate your LLM task management with Task Orchestrator, an MCP server that empowers you to define, organize, and track goals and tasks with hierarchical precision. Integrate intelligent task management into your workflow.

A Model Context Protocol implementation for managing and serving AI prompts with a TypeScript-based architecture in a monorepo structure.

A security-focused Model Context Protocol server for task management over stdio, backed by SQLite. It exposes tools to create, list, complete, delete, and search tasks with strict validation, parameterized SQL, confirmation for deletes, and full audit logging.

Enables AI assistants to manage Dida365 (滴答清单) tasks through the Model Context Protocol. It supports full task lifecycle management including creating, updating, and filtering tasks with automatic timezone handling.

Turns Claude-style skill folders into MCP tools, resources, and prompts for discovering and using skills.

Model Context Protocol server exposing system prompt files and summaries.

Records all MCP tool interactions in a centralized ledger, enabling developers to trace, replay, inspect, and audit AI agent workflows.

An MCP server for Task Trellis that provides tools for AI coding agents to manage tasks, currently featuring a simple hello_world demonstration tool.

An enhanced MCP server that fetches prompts from GitHub repositories with intelligent discovery, composition, and management features.

Enables Claude to interact with System Task projects, teams, and tasks, providing daily briefs, project reports, team load, and risk identification, as well as creating and updating tasks and demands.

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.

A local Model Context Protocol server providing backend tools for AI agents to manage projects and tasks with persistent storage in SQLite, enabling structured tracking of project tasks with dependencies, priorities, and statuses.