MCP servers

genpark-hubx-ai-apps-orchestration-skill

Enables agents to orchestrate and call tools across HubX's mobile AI app portfolio for multi-model chat, plant/vision diagnostics, generative image and design, nutrition tracking, audio note summarization, language tutoring, and wellness workflows through a unified MCP interface with structured JSON output.

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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.

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Enables deterministic reconstruction of complex tables from OCR bounding-box payloads, recovering multi-column layouts, merged headers, and grid hierarchies. It also supports formula consistency auditing and sensitive PII redaction for document processing pipelines.

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Enables fusing and reranking results from multiple search or retrieval sources using Reciprocal Rank Fusion and hybrid rank fusion. It provides deterministic scoring, robust parsing, and structured JSON output for autonomous agent workflows.

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genpark-hypothesis-welch-t-test-statistical-evaluator-skill

Enables statistical A/B testing by computing Welch's two-sample t-test, Satterthwaite degrees of freedom, and two-tailed p-values, alongside time-series forecasting, anomaly detection, regression, and PCA through MCP tools.

Official·1 source

Enables MCP clients such as Claude Desktop, Cursor, and Windsurf to run two-sample Welch's t-tests that compute Satterthwaite degrees of freedom and two-tailed p-values for A/B test comparisons. It also exposes zero-dependency statistical primitives including Holt linear forecasting, Z-score/IQR anomaly detection, gradient-descent regression, and power-iteration PCA, all using only the Python standard library.

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Enables autonomous financial agents and payment webhooks to create and verify immutable double-entry ledger entries, enforce debit-credit balance invariants, and generate hash-chained audit trails with fraud detection.

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Enables auditing influencer brand safety and multi-year controversy history through Traackr.

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Enables monitoring influencer brand share of voice and creator mention sentiment using Meltwater data, delivering deterministic structured output for MCP-compatible clients.

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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.

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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.

Official·1 source

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.

Official·1 source

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.

Official·1 source

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.

Official·1 source

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.

Official·1 source

genpark-jwt-hmac-sha256-token-attestation-skill

Enables agents to cryptographically attest and verify identity and execution state through RFC 7519 JWT HMAC-SHA256 signature checks, claims and expiration validation, constant-time secret comparison, SHA-256 Merkle inclusion proofs, Schnorr zero-knowledge identification, and sliding-window nonce replay protection. It runs as a zero-dependency Python MCP server over stdio, so it can be attached to Claude Desktop, Cursor, or Windsurf without external libraries.

Official·1 source

genpark-knowledge-graph-entity-relation-triplet-extractor-skill

Enables agents to build and query structured semantic graphs, extracting, traversing and multi-hop reasoning over entity-relation-object triples while producing Cypher and JSON-LD graph output. It also combines BM25 lexical and dense vector retrieval with rank fusion, semantic caching and context reordering for grounded agentic memory.

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Enables extraction of subject-predicate-object triples from text and multi-hop reasoning over connected data, producing Cypher and JSON-LD outputs for agent memory, vector search, and retrieval-augmented generation workflows.

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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.

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Enables MCP clients to resolve legal contract clause cross-references and build defined-term dependency graphs, flagging circular references and substituting statutory definitions. It also parses document layouts and complex financial/legal tables, returning structured matrices and audit telemetry with zero external dependencies.

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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.

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Enables agile sprint backlog velocity calculation and milestone completion prediction through the Model Context Protocol, integrating with MCP-compliant clients for deterministic, low-latency forecasting.

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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.

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Enables real-time voice agents to dynamically tailor speech pitch, cadence, and pause inflections through low-latency prosody, SSML, and emotion pacing modulation.

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