"author:alphaparkinc" matching MCP servers:

Enables agents to ingest payment webhooks and ledger transactions with cryptographic HMAC-SHA256 signature verification, sliding-window replay tolerance, and atomic idempotency locking. It also enforces double-entry debit/credit invariants and returns balanced journal records with audit and risk telemetry over MCP.

Enables MCP-compatible agents to execute bash and subprocess commands through a zero-dependency sandbox guard that blocks destructive system mutations, path traversals, reverse shells, and other unsafe execution patterns.

Enables autonomous agents to detect configuration drift, corrupted execution state, and missing runtime resources, then perform root-cause classification and automated snapshot rollback for self-healing runtime recovery.

Provides native MCP tools for quantitative-finance and risk-modeling workflows, enabling computation of option Greeks, simulation of geometric Brownian motion asset-price paths, historical/parametric VaR and CVaR estimates, bond duration/convexity metrics, and Nelson-Siegel yield-curve interpolation using only Python's standard library.

Enables MCP clients to guard agent API requests against nonce replay attacks using a sliding-window ledger, constant-time digest checks, JWT claim validation, Merkle inclusion proofs, and Schnorr zero-knowledge verification.

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 simulation of multi-qubit quantum circuits using complex state vectors, including unitary gates, entanglement, Born-rule measurement sampling, state fidelity, and probability distributions via MCP.

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.

Provides an MCP-compatible profiler for measuring edge and on-device LLM inference latency, including TTFT, TPOT, tokens-per-second throughput, and P50/P90/P99 jitter distributions. It enables agents and developers to run these telemetry benchmarks from MCP clients such as Claude Desktop, Cursor, and Windsurf.

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 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.

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 agents to maintain multi-tier memory across working scratchpad, dialogue buffer, and episodic stores with recency decay. Supports hybrid BM25/vector retrieval, rank fusion, graph expansion, and context reordering for optimized MCP-based memory and RAG workflows.

Enables MCP clients to compute shortest paths, topological dependency orderings, maximum network flows, minimum spanning trees, and PageRank-style centrality over graph topologies using only the Python standard library. It supports optimal multi-agent communication backbone and execution-plan analysis without external graph dependencies.

Enables distributed agents to synchronize a last-write-wins element set using conflict-free replicated data types, Lamport timestamps, and deterministic two-way state merge over MCP.

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.

Enables AI agents to record episodic events, decay stale transient interactions using an Ebbinghaus forgetting curve, and consolidate frequent episodes into enduring semantic user persona traits for long-term personalization.

Enables AI agents to construct SHA-256 Merkle trees and generate/verify inclusion proofs, validate JWT HMAC-SHA256 claims, run Schnorr zero-knowledge identification, and enforce constant-time secret comparison with nonce replay protection for tamper-evident state attestation. It runs as a zero-dependency, stdlib-only MCP stdio server for clients such as Claude Desktop, Cursor, and Windsurf.

Enables agents to cryptographically verify JWT HMAC-SHA256 signatures, validate claims and enforce expiration, and perform Merkle inclusion proofs, Schnorr zero-knowledge identification, and nonce replay protection. Runs as a zero-dependency stdio MCP server so clients like Claude Desktop, Cursor, and Windsurf can attest execution state without OpenSSL or external libraries.