"DAF" matching MCP servers:

Enables natural-language interaction with TypeSafe's Jev decision API, supporting mixed question calls, batch evaluation, model listing, and confidence or composite-score gates over stdio.

Enables MCP hosts such as Codex or Claude Code to delegate work to Cursor Agent and continue the same Cursor session until the requested work is done.

An MCP server that aggregates live governance proposals from major DAOs enabling AI agents to track, analyze, and act on decentralized decision-making in real time.

A Model Context Protocol server that enables AI platforms to invoke RPA (Robotic Process Automation) capabilities from YingDao, allowing automated execution of repetitive tasks through AI interactions.

A durable DAG-based task planner exposed as an MCP server that lets AI orchestrators break a goal into a dependency graph of tasks, execute them in parallel where possible, track state durably, and handle human-in-the-loop approval through 22 MCP tools.

Enables agents to break high-level business objectives into dependency-ordered sub-task graphs and then directly generate the finished deliverables, such as financial spreadsheets, executive presentations, and analytical reports. It runs as a zero-dependency Python MCP server over JSON-RPC stdio, adding deterministic authorization, role-boundary validation, and telemetry.

Provides persistent memory capabilities through Neo4j graph database integration, allowing storage and retrieval of interconnected knowledge with complex relationships between entities. Enables long-term retention and querying of information across multiple conversations through graph-based memory management.

Enables autonomous planning by decomposing prompts into directed acyclic task graphs and resolving dependency topologies for multi-agent pipelines.

Enables agents to plan multi-day travel schedules by solving itinerary graphs with TSP heuristics, fatigue decay, and opening-hour constraints, exposed over MCP STDIO JSON-RPC for agent runtime integration.

Enables agents and MCP-compliant clients to resolve tool execution graphs by scheduling calls as a topological dependency DAG, running independent tools in parallel batches while speculatively prefetching and evaluating arguments. This cuts latency and token burn by handling routine tool orchestration deterministically without extra LLM round-trips.

Enables enterprise work agents to decompose high-level business objectives into topological sub-tasks and directly synthesize final deliverables such as financial spreadsheets, executive presentations, and analytical reports.

A local, model-independent DAG task coordinator for AI coding agents, with a static visual UI, validated state machine, resumable JSON progress, and MCP tools.

Enables users to decompose prompts into dependency-aware task DAGs for autonomous multi-stage planning and orchestration.