"author:alphaparkinc" matching MCP servers:

Enables long-horizon autonomous task checkpointing and resumption through cryptographic event-sourced state persistence, idempotency key deduplication, timeout heartbeats, human-in-the-loop pause/resume tokens, and fail-safe recovery.

Enables AI agents to coordinate atomic distributed transactions across a swarm through a prepare/vote phase, unanimous commit-or-abort consensus, quorum validation, and failure recovery. It also layers in vector-clock causal stamping, LWW CRDT reconciliation, and gossip dissemination so multi-node state converges deterministically without external dependencies.

Enables autonomous agents to detect under-specified user goals, gauge reversibility and risk, and present minimal multiple-choice clarifications before committing to high-stakes execution.

Enables AI agents to run structured multi-persona debates over high-stakes decisions and merge their positions into an explainable consensus, complete with logged dissenting minority opinions. It runs as a zero-dependency Python MCP server or importable module, compatible with Claude Desktop, Cursor, and custom agent frameworks.

Enables clients to optimize prompt prefix caching by matching longest common token prefixes with a radix trie, eliminating redundant prefill computation and reducing time-to-first-token.

Enables personal AI agents to perceive ambient context, retain episodic memory with decay, act proactively, and route routine decisions through fast System-1 reflexes while escalating ambiguous multi-hop plans to frontier LLMs. Includes reversible execution checkpoints, token budgets, and local-first memory retention, and plugs into MCP clients like Claude Desktop, Cursor, and Windsurf.

Enables AI agents to combine ambient multimodal perception, continuous episodic memory, and recency-decay context briefing for fast deterministic decisions and safe tool routing. It integrates with MCP clients to reduce unnecessary frontier LLM calls while enforcing token budgets and local-first memory retention.

Enables MCP-compatible agents to arbitrate typed state-action schemas, dispatch routine actions deterministically, and apply pre-escalation filters that reduce unnecessary frontier LLM calls. It supports low-latency, schema-enforced routing with safety guardrails and local-first memory retention.

Enables autonomous agents to orchestrate HubX mobile AI tools for vision diagnostics, voice transcription, photorealistic image generation, and spatial design through a native MCP interface.

Enables zero-dependency INT8 symmetric quantization of LLM weights and activations on a per-tensor or per-channel basis, reporting SNR and MSE reconstruction telemetry along with TTFT/TPOT latency and jitter metrics. Also exposes edge inference primitives such as PagedAttention block allocation, radix prefix caching, and speculative decoding verification through the Model Context Protocol.

Enables MCP clients to run zero-dependency graph algorithms—including topological sorting, strongly connected component detection, shortest paths, maximum flow, minimum spanning trees, and PageRank—for agent dependency resolution and execution planning.

Enables autonomous web agents to prune DOM trees, canonicalize URLs against anti-crawler traps, auto-map form input schemas, and convert HTML/Markdown tables into typed JSON records. It also scores agent execution trajectories against gold benchmarks using action-level precision, recall, and Levenshtein sequence edit distance.

Enables personal AI agents to sense ambient attention cues, compute intent probabilities, and execute reversible guarded dry-run actions without prompting. It routes routine reflexes through a fast System-1 decision layer backed by decaying episodic memory and strict safety and privacy guardrails.

Enables AI agents to resolve typed routing, loop-stall arbitration, and tool safety checks locally at sub-millisecond latency, avoiding unnecessary frontier LLM calls. It enforces reversible execution checkpoints, token budgets, and decayed episodic memory for safe, privacy-first autonomous operation.

Deliversdependency-free slash unavailable:

Enables orchestration of worker registration, capability-based task dispatch, heartbeat telemetry, automatic circuit breaking, and checkpoint recovery for distributed agent operations.

Enables high-throughput semantic query caching and approximate deduplication by combining exact SHA-256 matching, cosine similarity threshold checks, and LRU eviction.

Enables agents to deduplicate semantic prompt caches by measuring Jaccard token overlap, preventing redundant LLM calls and reducing cost. It provides deterministic lookup-or-insert cache behavior through an MCP-compatible interface.

Enables AI agents and MCP clients to dispatch queries to the most suitable model among 70+ LLMs by scoring intent, budget, cost, and latency, returning structured routing decisions with execution telemetry. It runs as a zero-dependency Python MCP server that plugs into Claude Desktop, Cursor, and other MCP-compatible clients.

Enables deterministic multi-agent debate cross-examination and consensus arbitration through the Model Context Protocol, with structured JSON output and zero external dependencies.