Overtone News Mcp

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.

A Model Context Protocol server that provides access to Redis databases. This server enables LLMs to interact with Redis key-value stores through a set of standardized tools.

高德地图官方 MCP Server

Browser automation and web scraping

Write notes to Flomo

AI image generation using various models

MCP server to provide Figma layout information to AI coding agents like Cursor

Retrieving and analyzing issues from Sentry.io

Web and local search using Brave's Search API

Give your AI access to the source code and docs of all open-source libraries

An MCP server implementation that provides a tool for dynamic and reflective problem-solving through a structured thinking process.

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.

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.

Ask your AI agent why your tests are failing. Instant analysis on failures and regressions.

Full Android control from any AI agent — Claude, OpenCode, Windsurf, Cursor… 7 categorical MCP tools · 90fps live viewer · WiFi ADB · zero app to install

Architecture Diagrams Built and Maintained by AI

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

Deploy static sites on Hostsmith - give it a file, get a live HTTPS URL. EU/US residency.

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

Load and parse webpages using natural language queries to extract structured data. Manage browser sessions for interactive automation and retrieve usage metrics.

The Milvus MCP server enables AI applications to interact with Milvus vector databases using natural language commands. It allows AI models to perform vector searches, manage collections, and retrieve data without writing custom database queries. This integration facilitates seamless access to vector data, enhancing the capabilities of AI tools like Claude Desktop and Cursor.

The match graph for AI. Search 100,000+ capabilities across 13,000+ AI artifacts — models, agents, MCP servers, APIs, frameworks, CLIs, and more. Every query learns from the graph, making the next search smarter. 10 tools: search, find_mcps, get_artifact, compare, find_stack, resolve_capability, trust_passport, feedback, subscribe, unsubscribe. Install: npx -y @unfragile/mcp-server

MCP Advisor & Installation - Use the right MCP server for your needs

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.

Flag features directly from chat in your code editor, including VS Code, Cursor, Windsurf, Claude Code—any IDE with MCP support.