"Apache Spark" matching MCP servers:

An MCP server that optimizes Apache Spark code using Claude AI, providing intelligent code optimization suggestions and performance analysis.

MCP server exposing the Sovereign AI Blog (sovgrid.org), 44+ articles on self-hosted AI for NVIDIA DGX Spark. Three tools: search_blog, get_article, diagnose_sglang.

A tool that converts natural-language requests into full, deployable, execution-verified Apache Flink SQL jobs by orchestrating an LLM to draft specs, generate Flink SQL, and verify correctness against real Flink clusters and streaming semantics—including windowed aggregates, joins, Kafka grounding, and Deployable pipelines via the generate_flink_job, ground_kafka_topic, and deploy_to_kafka tools.

Carbon-aware MCP scheduler that defers "do it later" / "by tomorrow" / "overnight" LLM tasks to the cleanest electricity-grid hour inside a deadline — 40-70% lower CO2 per task, 50% cheaper via Anthropic/OpenAI Batch APIs. 9 MCP tools, per-task carbon receipts to local SQLite, Apache-2.0.

Builds a dynamic knowledge graph from your notes and uses LLM reasoning to discover blindspots, hidden cross-domain connections, track concept evolution, and spark creative inspiration.

Open-source framework for AI agents (Claude Code, Cursor, Codex, Gemini) to autonomously operate Google Ads, Meta Ads, and Search Console. Grounded in a local STRATEGY.md — not metric-chasing. Defense-in-depth security, local-first. Apache 2.0.

MCP gateway to World Labs Marble + Spark 2.0 for generating navigable 3D worlds from text, images, panoramas, multi-view sets, or video, and grounding a voice agent in scene coordinates.

An MCP server that wraps multiple vendors' Coding Plan services (iFlytek Spark, Volcengine) to provide code review, plan review, and multi-turn dialogue for AI coding tools like Claude Code.

Enables AI-powered document platform integration for Claude Code, Cursor, and Cline, providing session management, knowledge base, usage tracking, sheet operations, and Spark AI features.

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.

Enables statistical forecasting and anomaly detection over numerical time-series data using Holt linear smoothing, multi-step projections, and variance-based confidence bands.

Enables edge AI agents to optimize inference memory and latency by mapping logical blocks to non-contiguous physical pages, tracking fragmentation, and exposing related MCP tools.

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 MCP-compliant clients and autonomous agents to pick optimal few-shot in-context exemplars by combining semantic token overlap, cosine similarity, and Maximal Marginal Relevance diversity ranking for prompt optimization. Runs deterministically on the Python standard library with zero external dependencies, avoiding frontier LLM round-trips for sub-millisecond selection.

Enables inference engines to match longest common prompt token prefixes via a radix trie, so cached KV-cache blocks are reused instead of re-prefilled. This eliminates redundant prefill computation and lowers time-to-first-token, alongside paged attention allocation, speculative decoding verification, INT8 quantization, and latency telemetry.

Enables quant research organizations to log hypotheses and outcomes, count trials for honest statistical verdicts, explore novel methods, run significance gates, and evolve reusable skills from validated trajectories, all backed by SQLite.

Enables AI assistants to perform data analysis on local datasets through natural language, orchestrating Python, R, SQL, Spark, Jupyter, Power BI, and Tableau engines for statistics, machine learning, dashboard generation, and batch processing.

Enables users to perform multivariate linear regression with gradient descent, Holt linear forecasting, Welch's t-test evaluation, anomaly detection, and PCA dimensionality reduction through MCP-compatible clients using only Python standard library tools.

Local 395M decision model as an MCP server: pick one option from a list or answer yes/no about text, or run a recipe over a CSV, XLSX or folder. Runs offline. Apache-2.0.

Enables AI agents to generate safe, runnable HTML chart pages from structured JSON data using Apache ECharts. It provides tools for chart type recommendation, page generation, validation, and patching with controlled, deterministic output.