"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 quantitative risk analysis by calculating portfolio Value-at-Risk and Conditional VaR/Expected Shortfall across parametric and historical loss distributions, along with related option Greeks, Monte Carlo simulations, and fixed-income analytics through MCP.

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.

An MCP server that authenticates incoming webhooks from multiple providers such as Shopify, Stripe, GitHub, Slack, and PayPal using HMAC-SHA256 constant-time verification and timestamp replay-window validation. It lets agents validate and multiplex verified webhook events into dispatched event streams.

Enables agents to perform symmetric INT8 per-tensor and per-channel quantization of LLM weights and activations, returning SNR and MSE reconstruction metrics for edge inference optimization.

Enables translation of geometric bounding boxes, flowchart node connections, and architectural sketches into clean Mermaid graphs, JSON topology matrices, and boilerplate code scaffolds.

Enables autonomous agent runtime diagnostics and self-healing by detecting configuration drift, corrupted execution state, and missing runtime resources, then classifying root causes and executing snapshot rollback.

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 reversible anonymization of SSNs, credit cards, passport numbers, emails, and phone numbers in prompts before sending them to external LLMs, and restores original tokens from completions using local HMAC isolation.

Enables agents to dynamically generate typed MCP tool schemas, validate Python execution ASTs against unsafe imports, and hot-plug sandboxed callable handlers into an active runtime. This allows new tools to be synthesized and safely executed on demand.

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 coding agents to run deterministic, zero-dependency AST static analysis over source code, building call graphs and dependency metrics to find orphaned functions, unreachable methods, breaking API surface diffs, and token-bloat clusters, then return a structured refactoring plan. It plugs into MCP clients like Claude Desktop, Cursor, and Windsurf via a single Python entrypoint.

Enables constant-time cryptographic attestation, zero-knowledge verification, JWT HMAC-SHA256 validation, Merkle proof inclusion, nonce replay protection, and secure digest comparison for agent security credentials via MCP.

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 agents to resolve dependencies and analyze directed graphs through topological sorting, strongly connected components, shortest paths, maximum flow, minimum spanning trees, and centrality ranking via MCP.

Enables multiple autonomous agents to safely modify shared workspaces and documents by performing three-way reconciliation, detecting collisions, and returning structured conflict data for automated re-arbitration.

Enables agents to deterministically shrink multi-turn conversation history and prompts by stripping verbose boilerplate and filler tokens while preserving critical tool parameters and named entities. This lets users cut inference costs by roughly 40-60% without losing essential context, running either as a native MCP server over JSON-RPC stdio or as an importable zero-dependency Python module.

Enables agents to compute optimal multi-agent communication backbones and other graph operations such as shortest paths, topological sorting, maximum flow, and centrality ranking via the Model Context Protocol.

Enables MCP clients to run deterministic, zero-dependency recursive self-critique and reflection loops over a payload and optional options, iteratively evaluating and revising agent output to autonomously repair logic defects, test failures, and hallucinations. Supports direct JSON-RPC stdio integration into MCP-compliant clients such as Claude Desktop and Cursor.

Enables users to simulate multi-qubit quantum circuits through an MCP interface, including unitary gates, entanglement, Born-rule measurement sampling, fidelity/probability calculations, and Grover amplitude amplification. Integrates with Claude Desktop, Cursor, and Windsurf via JSON-RPC stdio.