"Pine Script v6 Python integration or comparison" matching MCP servers:

Document Comparison AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables AI agents to generate duration-aware, fact-checked video scripts from topics or transcripts by exposing tools for live news/web research, article fetching, and transcript ingestion, plus resources and style prompt templates.

Generates peer comparison reports on emissions trends, reduction targets, and gaps for companies in a given SICS sector/sub-sector using live data from the Climate Data Utility (CDU).

MCP server for IG's REST Trading API and Lightstreamer streaming API. Provides tools for accounts, markets, positions, orders, watchlists, sentiment, OTC trades, and streaming price/account/trade/chart data.

Enables natural language control of quantum photonics experiments through the qudi framework. Supports safe instrument control, measurement execution, and safety system management with built-in runlevel protection and parameter validation.

Enables querying Google NotebookLM notebooks directly from Claude Desktop (MCP) and Claude Code (CLI), providing citation-backed answers from Gemini.

Given a company domain or name, it checks ten app marketplaces — HubSpot, Salesforce AppExchange, Slack, Zapier, Shopify, Atlassian, Chrome Web Store, Microsoft, Google Workspace, and WordPress — to report where the company is listed, with each listing's URL, installs, rating, review count, and match confidence. Useful for competitive research and partner-ecosystem audits across many domains in a single run.

This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.

Enables AI agents to operate DSpace repositories through a typed interface, including SAF package building, metadata editing, scientometric harvesting, open-access PDF downloads, and repository administration.

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.

Enables secure execution of statistical Python code in isolated micro-VM sandboxes, generating charts and analytical summaries.

Enables AI agents to hire verified human operators for tasks requiring physical presence, human perception, or judgment, such as real-world verification, product testing, and data collection.

Search and load AI fine-tuning and evaluation datasets, prompts and few-shot contexts from the ContexteTech catalogue, grounded in official European sources, in 9 languages. Dataset download links (JSONL, ShareGPT, Alpaca, CSV) and cost estimates per model.

Provides AI agents with real-time access to corporate credit data, including debt structures, bond pricing, and guarantor chains extracted from SEC filings. It enables complex financial analysis such as screening companies by leverage, tracing corporate hierarchies, and searching covenant language.

Enables time series analysis following Box-Jenkins-Treadway methodology, supporting guided or autonomous modes for model identification, estimation, and diagnosis via an LLM.

Enables AI assistants to load CSV datasets, compute summary statistics, filter rows, rank columns, and compute correlations through the Model Context Protocol.

Enables analysts to build and evaluate simultaneous VARMA models for systems of time series via exact maximum likelihood on the ATSW ladder, using univariate models as the yardstick. It guides users through loading univariate fits, identifying residual cross-correlations, estimating joint models, forecasting, impulse responses, variance decomposition, and recording decisions.

Owner-side YouTube analytics for AI agents: watch time, traffic sources, audience retention, playlist and podcast-series metrics, and episode-over-episode comparison. Read-only, all of it.

A thin MCP server that wraps spicelib for circuit simulation. Exposes tools for running AC, transient, DC op, and parameter sweep analyses, enabling behavioral model fitting through iterative simulation and measurement comparison.

Give AI agents 10 offline tools for market observations, repeated headlines, forecast evaluation, correlation risk and statistical checks. Preserve source clocks, missing data and stated assumptions. Python, CLI and MCP share the same zero-dependency core; no feed collection or trade execution.

Enables LLM agents to query and analyze hh.ru job vacancies via full-text and semantic search, with tools for vacancy lookup, skill statistics, market overview, and candidate profile comparison.

Enables read-only search and analysis of your local Zotero library, including full-text PDF retrieval with OCR fallback, annotations, and cross-item comparison for synthesis.

Seed oil (PUFA) data for 500+ US restaurant chains: letter grades, the oil each chain fries in, cleanest menu items, and rankings. Zero-dependency Python stdio server backed by the free hosted Seed Oil Tracker endpoint, no key or account needed.

Enables chatting with your site's SEO using real data, including crawling, on-page auditing, Google Search Console performance, Core Web Vitals, and competitor comparison from MCP clients like Claude or Cursor.

Provides free Google Trends data (interest over time, term comparison, related queries, trending now, regional breakdown) to MCP-compatible AI clients without needing an API key.

Enables Claude to search, download, and summarize any of the 17,167 official BCRP statistical series, each figure computed in Python and returned with its source URL. It also supports a cross-country comparison tool backed by World Bank indicators for GDP, inflation, unemployment, and other metrics.

Exposes ESG/CSR data from Zei World to LLMs, enabling sector browsing, company search, ESG score comparison, and detailed evaluation criteria exploration through natural language.

A safety-bounded MCP server for discovering data-acquisition devices and performing finite analog-voltage and digital-input reads through NI's nidaqmx Python package.

Provides market-intelligence tools for researching AI employee opportunities across Egypt, Saudi Arabia, and UAE using Meta ads evidence, with capabilities for competitor analysis, comparison, and evidence retrieval.

The MCP server for AlphaForge — the agent-native quant CLI: write strategies in JSON, optimize with Optuna TPE, validate with walk-forward, export to TradingView Pine v6. This server lets your AI agent drive the whole pipeline over MCP.