"author:Alpha-Park" matching MCP servers:

Enables autonomous agent swarms to coordinate and reach deterministic convergence through epidemic dissemination, causal vector clocks, CRDT reconciliation, quorum-based leader election, and atomic two-phase commit over MCP. It also supports bounded network traffic via rate limiting and conflict-free replication.

Enables enterprise agent fleets to register workers, dispatch tasks by capability, monitor continuous heartbeat telemetry, trip automated circuit breakers, and recover from checkpointed state through a zero-dependency MCP server.

Enables autonomous web agents to prune DOM trees, canonicalize URLs, auto-map form inputs, and convert HTML/Markdown tables into typed JSON, then score execution trajectories with action-level precision, recall, and Levenshtein edit distance against gold benchmarks. Runs as a zero-dependency, stdlib-only MCP server over JSON-RPC 2.0 stdio for Claude Desktop, Cursor, and Windsurf.

Enables autonomous agents to snapshot execution-tree and context states, evaluate branch viability, and roll back to previous checkpoints when an exploration path fails without restarting from scratch.

Enables agents to evaluate multi-turn episodic memory buffers, compute multi-factor salience scores, and deterministically prune low-utility noise so retained context fits within model limits.

Enables graph pathfinding and network analysis through MCP, including Dijkstra/A* shortest paths, topological dependency resolution, maximum flow, minimum spanning trees, and PageRank centrality for agent planning.

Enables coordinating multiple speculative agent execution paths by tracking milestone validation scores and canceling inferior branches early to reduce inference overhead.

Enables MCP-compatible agents to audit reasoning trajectories for loops, deadlocks, and logical contradictions and to perform non-destructive checkpoint rollbacks.

Enables autonomous agents to detect under-specified, high-stakes user goals, assess their reversibility and risk, and construct minimal multiple-choice clarifications before executing. Runs as a zero-dependency MCP server or importable Python module that gates autonomous execution behind those clarifying questions.

Enables edge AI agents to optimize inference memory and latency by mapping logical blocks to non-contiguous physical pages, tracking fragmentation, and exposing related MCP tools.

Enables agents to compute shortest paths, topological orderings, maximum flow and minimum cut bottlenecks, minimum spanning trees, and centrality rankings for graph-based multi-agent routing. It runs as a zero-dependency MCP server using standard Python libraries.

Enables agents to synchronize, merge, and reconcile distributed state across nodes using conflict-free last-write-wins element sets with Lamport/vector-clock causality, token-bucket gossip dissemination, and quorum-based commit coordination. It runs entirely on the Python standard library and exposes these primitives to Claude Desktop, Cursor, and Windsurf over the MCP JSON-RPC stdio protocol.

Enables autonomous agents to keep long-running conversations within configurable token budgets by compacting message arrays, preserving system instructions and recent turns while distilling older history into concise summaries. This prevents context window exhaustion without external LLM dependencies.

Enables personal AI agents to fuse ambient multimodal perception with continuous episodic memory streams, routing routine reflexive actions at low latency while reserving heavier reasoning for ambiguous plans. Memory records apply Ebbinghaus recency decay with contradiction resolution and user-controlled deletion.

Enables agents to adjust message formality, brevity, tone, and technical depth based on channel context, user cognitive load, and sentiment feedback. It runs as a zero-dependency, MCP-compatible server for consistent communication across interactions.

Enables personal AI agents to route routine, reflexive decisions through fast deterministic System-1 cognition while reserving frontier LLM calls for ambiguous multi-hop plans. Combines ambient perception, decayed episodic memory with contradiction resolution, and proactive sensing behind reversible execution checkpoints, token budgets, and local-first memory retention.

Enables real-time sliding-window token velocity anomaly detection and adaptive rate-limiting through a native STDIO MCP server built entirely on the Python standard library. Exposes deterministic algorithmic checks that report anomalies and throttle behavior as JSON tool results for MCP clients such as Claude Desktop, Cursor, and Antigravity.

Enables personal AI agents to detect user attention cues, compute intent probabilities, and execute guarded dry-run actions through MCP, with low-latency reflex routing, episodic memory, and safety guardrails.

Enables agents to guard external API calls with circuit-breaker health checks, success/failure reporting, and recovery state inspection, including exponential backoff with jitter and fail-fast rejection.

Enables autonomous agents to orchestrate HubX mobile AI tools for plant and nutrition vision diagnostics, voice transcription and summarization, photo-realistic portrait and art generation, and interior/exterior spatial design through structured JSON outputs.