"author:alphaparkinc" matching MCP servers:

Enables users to compute European option prices and sensitivity metrics, run Monte Carlo GBM simulations, calculate historical/parametric VaR/CVaR, evaluate bond duration and convexity, and interpolate Nelson-Siegel yield curves through MCP.

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 agents to perform Schnorr zero-knowledge identification and cryptographic attestation through a native MCP interface, including commitment-challenge-response proofs, JWT validation, Merkle inclusion checks, and replay protection.

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 agents to track causal relationships, reconcile concurrent state changes, and coordinate consensus across distributed networks using vector clocks, CRDTs, gossip, and quorum-based protocols.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

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.

Enables agents to compute shortest paths with Dijkstra and A*, resolve DAG dependencies, solve maximum flow, build minimum spanning trees, and rank graph centrality using JSON-RPC MCP tools.

Enables users to perform zero-dependency quantitative finance and risk analytics through MCP, including Nelson-Siegel yield curve interpolation, option Greeks, Monte Carlo simulations, VaR/CVaR, and bond duration/convexity calculations. It supports natural-language financial modeling and hedging strategy generation in MCP-compatible clients.

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 compute shortest paths, topological dependency order, maximum flow and minimum-cut bottlenecks, minimum spanning trees, and PageRank centrality over weighted graphs for routing and execution planning. It exposes these graph-analysis capabilities as MCP tools through a zero-dependency Python server.

Enables users to simulate quantum circuits, evolve qubit state vectors, generate EPR Bell pairs, perform Born-rule measurement, and run Grover amplitude amplification through a zero-dependency MCP interface.

Enables graph analysis over interconnected agent networks by computing centrality and authority scores, shortest paths, dependency orderings, maximum flows, and minimum spanning trees through MCP tools.

Enables agents to hash execution logs into tamper-proof Merkle trees, compute cryptographic inclusion proofs, and verify audit integrity through MCP tools.

Deliversdependency-free slash unavailable:

Enables coding agents to analyze monorepo dependency graphs, detect and resolve circular dependencies, and plan a topological build order via Tarjan SCC and Kahn algorithms. It exposes deterministic, zero-dependency Python AST static analysis over MCP for auditing and refactoring codebases.

Enables local zero-knowledge encryption of a personal vault and sanitization of PII such as credentials, credit cards, and addresses before dispatch to cloud LLMs, with client-side rehydration of masked data.

Enables AI agents and MCP clients to estimate VRAM footprint and tensor parallelization requirements for quantized llama.cpp models, returning structured outputs without external dependencies.

Enables deterministic SQL query synthesis from tabular schemas and query verification via MCP, with zero external dependencies.