"author:alphaparkinc" matching MCP servers:

Enables users to simulate quantum circuits, evolve qubit state vectors, apply gates such as Hadamard and CNOT, perform Born-rule projective measurements with Monte Carlo shot histograms, and run Grover amplitude amplification through MCP-compatible clients.

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 agents to run zero-dependency statistical modeling and data analysis through MCP, including multivariate linear regression via gradient descent, anomaly detection, time-series forecasting, hypothesis testing, and PCA dimensionality reduction.

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 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 run statistics and data-science computations over numerical streams using only the Python standard library, including Z-score/IQR anomaly detection, Holt linear forecasting, Welch's t-test hypothesis evaluation, gradient-descent multivariate regression, and power-iteration SVD/PCA dimensionality reduction. Exposes these capabilities over JSON-RPC 2.0 stdio so clients like Claude Desktop, Cursor, and Windsurf can project high-dimensional feature vectors into principal components without any external dependencies.

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 manage LLM inference memory through virtual paged-attention KV-cache block mapping, non-contiguous physical page allocation, zero-copy fragmentation tracking, radix-trie prefix caching, INT8 quantized compute, and speculative decoding verification. It also exposes prefill and decode latency telemetry so edge deployments can be benchmarked and tuned without external dependencies.

Enables agents to forecast numerical trends with zero-dependency Holt linear exponential smoothing, multi-step horizons, variance confidence bands, and supporting statistical anomaly detection, regression, hypothesis testing, and PCA.

Deliversdependency-free slash unavailable:

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 agents to ground and compare visual specifications by calculating physical dimensions and degrees of freedom from multimodal inputs.

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 MCP-compliant clients to run deterministic customer churn prediction and 12-month CLV regression synthesis through a zero-dependency Python skill.

Enables distribution-free split conformal prediction through MCP, producing adaptive prediction sets with finite-sample marginal coverage guarantees for agent routing, classification, and structured decision tasks.

Enables estimating LLM hallucination and confabulation by clustering sampled responses into semantic equivalence classes and computing semantic entropy. It provides a plug-and-play validation filter for agent pipelines using the MCP protocol.

Enables agents to orchestrate speculative decoding runs and deterministically verify draft-model acceptance probabilities via MCP.

Enables zero-dependency statistical analysis of numerical and time-series streams, flagging outliers via standard Z-score, modified median absolute deviation (MAD), and Tukey IQR fences. Also supports trend forecasting with Holt linear smoothing, multivariate gradient-descent regression, Welch's t-test hypothesis testing, and Power Iteration PCA dimensionality reduction through a native MCP stdio interface.

Enables deterministic detection and neutralization of adversarial prompt injections and override attempts in AI agent workflows via a zero-dependency MCP server, providing structured telemetry and low-latency validation.