"Sunsama: A Productivity and Task Management Tool" matching MCP servers:

Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with any MCP-compatible client.

Proxies a remote MCP server to add protocol-native background task support, including task management and progress forwarding, while preserving synchronous calls.

MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.

Async MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.

An MCP server for task orchestration.

This MCP server enables agents to manage complex tasks by providing tools for registration, complexity assessment, breakdown into subtasks, and status tracking throughout the task lifecycle.

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.

Provides tools for coordinating AI agent work on one machine through a local SQLite-backed queue and kanban, including task creation, atomic claiming, lease renewal, completion/failure, listing, comments, and cancellation.

MCP server that routes natural language requests to structured tool calls using a LoRA-tuned small language model, with built-in validation, retry, and fallback recovery.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.

Enables AI agents to request an independent second opinion from a different frontier model, with PII scrubbing before transit, live web search grounding, caching, and multi-provider failover.

Enables AI coding agents from different MCP-capable clients to exchange messages in real time through a shared in-memory broker. Supports agent registration, inboxes, and live event streaming without requiring a database.

Enables creation and management of scheduled tasks with interval, cron, or one-time triggers. Persists tasks in SQLite and supports MCP sampling to automatically invoke AI agents when schedules trigger.

Exposes an agent orchestration task queue as an MCP tool interface, allowing agents to submit, list, get, and update tasks with typed validation.

An MCP server that integrates with AI editors like Cursor to maximize agentic capabilities while solving context window limitations, providing a Svelte UI for task planning and implementation tracking.

Enables AI agents to discover and execute tools via a secure MCP server with JWT authentication, RBAC, rate limiting, and audit logging.

Enables reviewing and dispatching existing Codex tasks across projects through a lightweight control panel, including scanning task inventories, confirming Continue or Optimize prompts, and tracking dispatch progress.

Enables AI agents to run durable, isolated, and observable browser automation tasks via Playwright, with persistent profiles, recovery, evidence capture, and unattended execution for long-running web workflows.

Provides a self-hosted shared work queue for AI agents via MCP, using a local SQLite database for task management.

An MCP proxy that embeds tool definitions and returns only the top-k semantically relevant tools per query, reducing context usage and routing calls to upstream MCP servers.

Central registry for AI tools with vector search and knowledge graph, enabling intelligent tool discovery, chaining, and execution via MCP.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.

A single MCP server that provides dynamic access to thousands of APIs through discovery and proxy, eliminating the need to install and configure multiple separate MCP servers.

Enables AI agents to report task lifecycle events—start, progress, waiting, rename, and terminal outcomes—so a single Huawei Today screen card stays updated per task under one task_id. Delivers these notifications through a self-hosted Cloudflare Workers deployment with OAuth-scoped agents, queued retries, and an admin console for notification policies.

Enables centralized proxy and orchestration of MCP servers, providing unified tool discovery, authentication, and secure request routing. It adds a human-in-the-loop approval gate for high-risk actions and audit logging.

Enables MCP clients to batch repetitive web admin work by harvesting queue items read-only, collecting verdicts, and applying them only after human approval, with dry runs, staged rollout, rate limits, a circuit breaker and an audit log. Also provides general browser control — navigating, reading, filling forms and clicking on allowed sites through an already logged-in local browser, pausing for confirmation before irreversible actions.

Enables MCP clients and agents to create, list, and update the status of to-do tasks, while restricting them to least-privilege operations.

Enables declaring a catalog of tools for LLMs, filtering them by role, validating arguments, guarding sensitive calls with server-side confirmations, and recording an audit log.