"Python context management and dependency solving in ComfyUI" matching MCP servers:

A local, read-only MCP server (plus CLI) over your own stock/ETF transaction log. A deterministic core computes drawdown-first risk with bootstrap confidence intervals and validates targets with walk-forward verdicts. The assistant narrates, but the number fence makes it structurally impossible for the model to produce a figure.

Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.

A daily feed of AI releases, tools and techniques, each with a verification status, source trust score and evidence links. Read-only MCP server, no sign-in or keys.

Enables MCP clients to look up domains, URLs, IPs/ASNs, email addresses, phone numbers, crypto wallets, and business records; run autonomous investigations; collect crawl artifacts; and perform evidence-backed scam checks with traceable provenance.

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.

Provides access to ancient text corpora via stdio, enabling corpus listing, status checks, searching, and text/metadata retrieval with provenance envelopes.

Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.

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

Provides source-grounded Christian research material with exact passages, provenance, and stable citations via a REST API.

Provides provenance-aware access to selected Sefaria Bible context, including Targumim, Mishnah, Jewish commentary, Josephus/Philo, and lexicon lookups, with on-demand caching of long-tail Sefaria texts.

Read-only MCP server for the July 2026 survey of AI in open-source design systems, enabling agents to query 19 systems' affordances, coercion techniques, and platform data via 9 tools, 2 resources, and 2 prompts.

Give AI agents cited funding, money-market, FX and capital-market evidence through 16 public MCP tools. Keep source dates, freshness checks and coverage gaps attached; China macro coverage is metadata-only. No API key is required for the public surface. Research context only; no trade execution.

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.

A local stdio MCP server that gives Claude on-demand access to any Kaggle competition's brief, rules, evaluation, data description, leaderboard, top discussions and public notebooks, using the user's own Kaggle credentials and a disk cache with no hosted infrastructure. It also exposes a live tool for spotting forum topics and comments posted since the last fetch.

Expert-curated knowledge graphs, corporate earnings intelligence, and voice-studied Human Agents for AI agents. 55 tools across 220+ domain graphs with structured insights, relationship traversal, and live institutional data streams.

Enables MCP-capable agents to turn a small business's public site summary and a few cultural seed terms into a taste-context brief, resolving seeds into cross-domain taste entities in music, film, brands and places, then returning a labelled fact table, inference-marked messaging ideas and a tool-call trace. It exposes a tool-call pipeline plus a scenarios listing over Streamable HTTP, with a fixtures mode that serves clearly labelled sample data when live Qloo calls are unavailable.

Enables semantic retrieval over a structured Quran and classical exegesis library, returning relevant source chunks and verified references for thematic research queries.

A Python MCP server for querying grant applications and previewing or committing scoped reviewer reassignments.

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

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

Classifies company sites as B2B or B2C and as SaaS, online store, agency, manufacturer, wholesaler, distributor, or marketplace, while matching your own buyer phrases and returning the quoted text and page as evidence. Runs deterministically on Apify, giving one row per domain with grace-free status rows for unreachable, blocked, or empty sites.

Real human judgment as agent tools -- an AI agent can ask a question and get back a structured, schema-validated JSON answer from a real quality-scored human. 16 response types (yes/no, ratings, rankings, A/B tests, sentiment, image/video/audio review, voice/video/photo capture). Fully programmatic signup with a $5 free trial credit, no card required.

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.

Deterministic US equity-compensation tax optimizer. Six tools cover ISO/AMT exercise scheduling, NSO sell-vs-hold, RSU vest-and-sell, single-stock concentration, protective put / zero-cost collar pricing, and Section 1202 QSBS qualification. Federal plus 50-state plus DC tax math.

One-call Australian health and welfare data plumbing via AIHW — cited responses for practical analysis, not a data broker.

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.

Enables users to pull portfolio company lists from Y Combinator, a16z, Sequoia, Accel, and other investors or any portfolio page URL, returning one row per company with name, domain, website, description, and stage or batch. Runs on Apify and produces Clay-ready output, with YC results filterable by batch, industry, region, status, and search terms.

Institutional-grade quantitative stock analysis and research signals for AI agents via the Model Context Protocol (MCP).

An MCP-native pipeline for collecting structured intelligence on higher education institutions using the WHED schema, enabling scraping, extraction, validation, and saving of profiles.

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.