Monad Magma MCP

Official MCP server for 100Hires ATS: candidates, jobs, applications, interviews, messages.

Institutional cross-border settlement rail for AI agents — any currency pair, ~75-120 bps via Base mainnet. 11 tools: quote, ESG scoring, oracle status, fee verification, competitor benchmarking, and on-chain settlement. ESG-linked fees, MiCA/FCA/Basel III-aligned, on-chain verifiable receipts.

A Model Context Protocol (MCP) server for image generation and manipulation using fal.ai's Stable Diffusion model.

Plug Claude Desktop, Claude.ai, Cursor and any MCP-compatible AI agent straight into HUHU's phone spam database. One URL, your API key, and your AI can verify numbers, read your balance and browse history — without leaving the chat.

Canonical vocabulary server for autonomous business design. Exposes the Arco Lexicon as seven MCP tools: term lookup, related terms, alignment verification, citation formatting, source retrieval, term listing, and term suggestion. No authentication required. Streamable HTTP transport.

Search, rank, and compare 500K+ AI models, datasets, papers from 13+ platforms. Hardware-aware model selection with VRAM/license constraints. FNI-scored rankings with full factor breakdown.

Curated audio-news MCP server. Search trending articles, fetch narrated audio, subscribe topic feeds. OAuth 2.1 + RFC 7591 Dynamic Client Registration. Free tier; premium briefings via x402 over stablecoin settlements.

Self-hosted semantic memory layer for Claude and MCP-compatible AI clients. Store notes, search by meaning not keywords, and recall relevant context automatically. Built on Cloudflare Workers, D1, Vectorize, and Workers AI. Runs on the free tier. One-click deploy.

Generate n8n workflow JSON (including LangChain AI agents), scaffold custom TypeScript nodes, and lint workflows for missing credentials, deprecated types, and broken connections.

Write notes to Flomo

GitLab API, enabling project management

TubePull is a YouTube video downloader exposed as an MCP server. One tool, download_video, takes a YouTube URL plus a format (mp3/mp4) and an optional quality, runs the download on TubePull's servers, and returns a short-lived signed download link. Free tier allows 3 downloads per 24 hours per client; Unlimited plan ($3.99/mo) removes the cap and unlocks 1440p/4K plus videos over 2 hours.

פודקאסט על AI, למידת מכונה, בינה מלאכותית, תכנות וטכנולוגיה

An MCP server and REST API that classifies Polymarket wallets as human or bot, scores their trading edge from 0–10, and streams their current open positions. Built for AI agents on copy-trading and signal-following stacks. # Use it from any MCP client (Claude Desktop, Cursor, etc.) pip install polymarket-intel-mcp polymarket-intel-mcp # Or call the hosted REST API directly curl https://polymarket-intel-production.up.railway.app/wallet/0xf1528f12e645462c344799b62b1b421a6a4c64aa How this fits with other Polymarket MCP servers There are several MCP servers covering Polymarket, each at a different layer: Server What it does When to use it polymarket-intel (this) Wallet intelligence — classify human vs bot, score trading edge, read open positions Deciding whose signals to follow graph-polymarket-mcp Market data via The Graph subgraphs (20 tools, 8 subgraphs) Reading raw on-chain market data whitmorelabs/polymarket-mcp Slippage, liquidity, arbitrage, price feeds Pricing your own trades joinQuantish/polymarket Self-hosted trading agent Running an autonomous bot These complement each other. A copy-trading agent would use polymarket-intel to filter wallets worth following, then graph-polymarket-mcp to read the markets those wallets are betting on, then whitmorelabs/polymarket-mcp to size its own entries. What it answers "Is this trader a human or a bot?" — score_polymarket_wallet(wallet_address) → returns classification ∈ {human, bot, insufficient_data} plus a confidence score and reason codes. "Do they actually have an edge?" — edge_score from 0–10, gated on net realised PnL so distributed-but-losing wallets don't get false positives. "What are they betting on right now?" — get_open_positions(wallet_address) returns live positions sorted by size, refreshed every 30s. "How has their edge changed over time?" — /wallet/{address}/history returns the score time series from the daily snapshots. Why this exists The Polymarket leaderboard is misleading. It includes unrealised PnL marked-to-current-price, so the names at the top are dominated by bots running structural arb plus a few wallets sitting on huge open positions that may never resolve in their favour. Agents that copy-trade naively from the leaderboard get burned. This service runs every leaderboard wallet through behavioural fingerprinting (focus ratio, holding period, timing regularity, category concentration) plus PnL reconstruction from raw activity, and only surfaces traders that look like genuine humans with a real edge. The dataset grows more valuable over time — every day the snapshot job runs, historical signals accumulate. Wallets that have been consistently above edge 7 for 90 days are a stronger signal than any single point-in-time score. Distributed as both a REST API and an MCP server Surface Use case Setup MCP server Agent that needs tool-style access pip install polymarket-intel-mcp REST API Custom HTTP integration, dashboards curl https://polymarket-intel-production.up.railway.app/... Hosted MCP Agent on any MCP-compatible client Add https://polymarket-intel-production.up.railway.app/mcp to client config Architecture ┌──────────────────────────────────────────────┐ │ core/ │ │ client.py — Polymarket data API client │ │ signals.py — pure signal calculators │ │ scorer.py — classifier + edge score │ │ models.py — Pydantic response schemas │ ├──────────────────────────────────────────────┤ │ db/ │ │ schema.sql — Postgres tables + indexes │ │ repository.py — Repository protocol + │ │ InMemoryRepository │ │ supabase_repo.py — Supabase impl │ │ converters.py — ScoreResult ↔ records │ ├──────────────────────────────────────────────┤ │ api/main.py — FastAPI HTTP server │ │ mcp_server/ — MCP server (stdio) │ │ scripts/ │ │ analyze_wallet.py — CLI │ │ snapshot_job.py — daily cron entry │ │ tests/ │ └──────────────────────────────────────────────┘ Core has no idea persistence exists. The API and snapshot job depend on the Repository protocol — Supabase in production, in-memory in tests and when env vars are unset. This is what makes the suite run without a database and what lets you swap Supabase for Neon, RDS, or anything else later by adding one file.

Automatically create a remote browser to complete your specified tasks, developed based on Browser Use + Sandbox. 自动创建一个远程浏览器，完成你指定的任务，基于Browser Use + Sandbox开发。

Monitors Procore/Autodesk for project status changes and notifies stakeholders via Slack/Teams. Free MCP server by Espergrid. Enterprise implementation available. *Need custom deployment? espergrid.com offers white-glove implementation starting at $1,499.*

Model Context Protocol server that integrates AgentQL's data extraction capabilities.

Official Firecrawl MCP Server - Adds powerful web scraping to Cursor, Claude and any other LLM clients.

基于七牛云产品构建的 Model Context Protocol (MCP) Server，支持用户在 AI 大模型客户端的上下文中通过该 MCP Server 来访问七牛云存储资源、利用 Dora 服务进行图片操作等。 如果有什么需求欢迎在下方评论，您也可以在 github 仓库中提 issue。

Cambium PM MCP turns Claude into a product workflow tool by bringing synthetic personas, grounded in public demographic and market data, into your sessions. Use it to validate ideas, pressure-test launch plans, and triage backlog items against different segments. Tools included: research_product_idea — runs a research job that interviews a generated persona panel, pulls in web research and demographic context, and returns a report set_business_case / get_business_case — store and recall the strategic context for your project record_decision — capture product decisions and their rationale across sessions set_phase — track whether you're in discovery, build, or scale recall_context — load prior session state at the start of a new chat end_session — store a continuity summary for next time Best for early-stage validation and pre-launch sanity checks. Not a substitute for talking to real users once you have them.

Manage logistics operations by comparing shipping prices and creating shipments across multiple agencies. Track package status in real-time, download labels, and handle cancellations through a unified interface. Streamline fulfillment workflows by accessing saved addresses and monitoring bulk delivery tracking updates.

Create a remote sandbox that can execute code/run commands/upload and download files. 创建远程沙盒，可以执行代码/运行命令/上传下载文件

Repository management, file operations, and GitHub API integration

Read-only database access with schema inspection

Secure file operations with configurable access controls

GBOX provides environments for AI Agents to operate computer and mobile devices. Mobile Scenario: Your agents can use GBOX to develop/test android apps, or run apps on the Android to complete various tasks(mobile automation). Desktop Scenario: Your agents can use GBOX to operate desktop apps such as browser, terminal, VSCode, etc(desktop automation). MCP: You can also plug GBOX MCP to any Agent you like, such as Cursor, Claude Code. These agents will instantly get the ability to operate computer and mobile devices.