"Cool MCP (Model Context Protocol) tools for DevOps" matching MCP servers:

An MCP server that integrates AI retrievals with NASA's Common Metadata Repository (CMR), allowing users to search NASA's catalog of Earth science datasets through natural language queries.

A Python implementation of the Model Context Protocol (MCP) server that enables searching and extracting information from arXiv papers, designed to be extensible with additional MCP tools.

Connects to wetlab protocol resources such as protocols.io, enabling users to access and manage experimental protocols via natural language.

Access to many public datasets right from your LLM application.

Provides local, evidence-aware research memory with human review, revision history, and an inspector, allowing assistants to preserve source reports, inferences, assumptions, and evidence across sessions.

Provides actionable financial intelligence tools for AI agents including insider buying signals, earnings IV plays, market pulse, stock analysis, and options strategies via free public data sources.

An MCP server for managing pandoc/CSL-YAML bibliographies and verifying citations in documents.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.

An MCP server that enables users to read, search, and analyze PDF documents. It provides tools for extracting text, viewing metadata, searching content with context, and generating word statistics.

An MCP server that provides comprehensive financial insights and analysis by leveraging real-time market data, news, and advanced analytics for stocks, options, financial statements, and economic indicators.

An MCP server for global tech intelligence gathering, offering tools for searching Chinese and English web content, GitHub trending, arXiv papers, HN, Exa, SEC filings, Juejin, Zhihu, Bilibili, and A-share financial reports, all without API keys and with direct China access.

Enables AI agents to perform corpus analysis tasks such as discovery, search, and data access via the Model Context Protocol.

Provides honest A/B test verdicts using interactive cards, with statistical calculations for conversion rates, sample sizes, and Bayesian probability-to-beat.

Enables any MCP-capable agent to call Kimi Code's official managed web search, URL fetch, and 25+ professional data sources (Wind, SEC EDGAR, S&P, World Bank, IMF, FRED, Tianyancha, arXiv, Scholar, Chinese law & standards, WHO/FAO/OECD, Xinhua, Caixin) through a zero-dependency stdio server. Each user signs in with their own paid Kimi account via OAuth device flow, and credentials are stored locally with tokens never exposed to agents.

Evidence-first YouTube research server enabling search, transcript citation, video analysis, flashcards, and collateral generation for MCP hosts and VS Code.

MCP server for Jupyter notebook navigation and editing. Reads .ipynb files directly as JSON, providing efficient tools for reading, searching, and modifying cells without needing a Jupyter server.

Presents a plausible, tool-rich workspace surface — file reading, credential listing, command execution, database queries, and configuration retrieval — that returns only synthetic results while logging every connection, tool call, and argument received. It enables researchers to measure what MCP clients and agents actually do when handed dangerous-sounding tools, including whether they follow injected instructions or complete a retrieve-then-use credential chain.

Local-first MCP server that gives Claude Code web search, page reading, video transcription, and image analysis — without paid API keys. Runs SearXNG + whisper.cpp natively on Apple Silicon for zero-cost, low-latency research workflows.

Provides five rigorous reasoning protocols (debate, red team, audit_argument, threat_model, check_study) that run on the AI you're already using, requiring no extra API keys or costs.

Enables running the same prompt(s) across roughly 300 models from Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, Qwen and others, then comparing per-model outputs, latency, token usage, error rates and billed cost side by side. Supports free cost estimates before launching, asynchronous run creation with status polling and cancellation, prepaid x402 credit top-ups, and an optional AI-written comparison summary of the results.