"Apache Spark" matching MCP servers:

Enables LLM agents to interact with Apache Zeppelin notebooks via the Zeppelin REST API, supporting operations like listing, searching, editing, and running notebooks.

Bridges Spark ASX trading platform with Claude Desktop via MCP, enabling natural language queries for live quotes, positions, P&L, and market analytics.

Read-only MCP server exposing AGmind Systems Lab's measured local-LLM benchmark claims (TTFT, inter-token latency, task success, etc.) on AMD Strix Halo and DGX Spark hardware via search, claim lookup, and measured-combination listing tools.

MCP server for chatting with physical-world data from robotics, drones, automotive, and IoT sources using natural language. It generates auditable SQL queries over Apache Arrow/DuckDB to let you analyze, summarize, and build data pipelines.

Three composable MCP servers for clinical AI — FHIR R4 read/search/validate, medical terminology (LOINC/SNOMED/RxNorm/ICD-10), and drug safety reasoning (interactions, dose check, allergy). Apache-2.0, production-ready.

An MCP server for querying and analyzing Apache Incubator podling health reports. It provides tools to list, search, and compare podling metrics across different time windows from Apache's health report Markdown files.

An MCP server for accessing and analyzing Apache Software Foundation Incubator podling data from podlings.xml. It provides tools to query podling metadata, generate statistics, and analyze incubation trends over time.

Open scientific knowledge MCP for AI agents. Three profiles: search (15 tools incl. find_evidence, compare_papers, explore_topic), publish (5 tools for direct submission with AI-assisted review), govern (20 tools for proposals, voting, methodology shaping).

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 natural language querying and management of Apache Superset dashboard metadata, including user activity, data lineage, and change tracking.

Read-only biomedical MCP server connecting PubMed, ClinicalTrials.gov, ClinVar, gnomAD, OncoKB, Reactome, KEGG, UniProt, PharmGKB, CPIC, OpenFDA, Monarch Initiative, GWAS Catalog, and more. One command grammar for all biomedical entities — genes, variants, diseases, drugs, trials, articles, phenotypes, pathways, proteins, diagnostics, and adverse events. 27 tools. Apache-2.0 license.

Historical geography for AI agents: 1,000+ polities with real GeoJSON borders, dynastic succession chains, events, cities and trade routes from 4500 BCE to today, each with cited academic sources and a confidence score. Wraps the free AtlasPI REST API (https://atlaspi.it) as 39 MCP tools — no API key required, Apache-2.0.

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.

Enables users to calibrate multimodal chart coordinates and recover tabular datasets from visual bar, line, and scatter charts as structured numbers.

Enables AI agents and MCP clients to heuristically parse multi-column document dumps, aligned spacing, and CSV/TSV input, converting them into clean GitHub Flavored Markdown tables and typed JSON. Runs fully offline on the Python standard library with zero external dependencies, returning deterministic results without LLM round-trips.

Enables agents to run zero-dependency statistical analysis natively over MCP, including Power Iteration PCA/SVD dimensionality reduction, Holt linear forecasting, modified Z-score and Tukey IQR anomaly detection, Welch's t-test, and multivariate gradient-descent regression on numerical data streams. Projects high-dimensional feature vectors into principal component representations without any external libraries.

Enables AI agents to analyze and link citations across multiple research sources, building cross-citation graphs and resolving consensus from conflicting findings. It exposes this capability as a deterministic, zero-dependency MCP server that plugs into Claude Desktop, Cursor, and other MCP-compliant clients.

Enables AI agents to perform deep research across multiple sources, arbitrating consensus while evaluating source authority and detecting contradictions. It integrates via MCP with clients like Claude Desktop and Cursor to deliver deterministic, structured research dossiers.

Enables users to audit academic citation contexts, calculate scientific consensus ratios, and generate credibility badge indexes for verified citations through MCP-compliant tools.

Enables AI-powered ROI prediction and tracking with Monte Carlo simulations, Dutch market validation, and multi-project comparison. Supports natural language input for financial projections, risk analysis, and industry benchmarking across sectors like finance, healthcare, retail, and manufacturing.