Footballbin Mcp Server

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

GitLab API, enabling project management

petromcp is a small MCP server that lets Claude (or Cursor, or Codex CLI, or anything else that speaks MCP) read .las well log files directly. If you have ever tried asking an LLM about a well log you know the workflow today is to copy-paste curve values into the chat. Petroleum file formats are binary or semi-structured and the hosts can't open them. The current release wraps lasio (the established open-source LAS parser) and exposes five tools: read_las_file, summarize_las_curves, read_las_curve, compare_well_logs, and convert_units. There is a strict path allowlist by default, the server makes zero outbound network calls, and the only data that leaves your machine is whatever the host application would have sent to its language model anyway. DLIS, SEG-Y headers, and pump card data are queued for the next releases. Repo, install instructions, and tool reference are in the README. Feedback on the QC heuristics from anyone with a petrophysics background would be particularly welcome. They are defensible defaults but I would rather have them shaken out than discover problems after a launch demo. If you have the repo cloned, the server config is: { "mcpServers": { "petromcp": { "command": "uv", "args": [ "run", "--no-sync", "--project", "/absolute/path/to/petromcp", "petromcp", "serve" ] } } } Else, you can use { "mcpServers": { "petromcp": { "command": "uvx", "args": [ "--from", "git+https://github.com/ameyxd/petromcp", "petromcp", "serve" ] } } }

Headless web analytics for AI agents. A broad range MCP tools for traffic, events, funnels, conversions, sources, and performance. No dashboard — agents query structured JSON directly.

An MCP service designed for deploying HTML content to EdgeOne Pages and obtaining an accessible public URL.

The only crypto MCP server with affiliate revenue share for AI agent developers. Permanent 20-40% trading fee discounts on Binance/OKX/Bybit/Bitget/Gate.io/Hyperliquid/Aster DEX via top-tier referral codes. 6 tools with built-in country compliance.

A Model Context Protocol server that provides time and timezone conversion capabilities. This server enables LLMs to get current time information and perform timezone conversions using IANA timezone names, with automatic system timezone detection.

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

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。

Web content fetching and conversion for efficient LLM usage

MCP wrapper for Askew's x402 endpoints — 8 tools (3 free, 5 paid in USDC on Base): live DeFi yield comparison, SOL/ATOM staking routing, agent-economy research search, Guardian security threat intel, and a combined strategic feed.

AI tools show when you sent a message. Chron logs when the AI responded too — and keeps a permanent, queryable record of every exchange across every tool you use. Works with Claude Desktop, Claude Code, Cursor, Windsurf, and any MCP-compatible AI tool. Every exchange is stored with precise local timestamps (including timezone offset) to a SQLite file on your machine. Query it directly, export it, or use it to answer: what did the AI say, and exactly when?

An MCP server implementation for retrieving information from the AWS Knowledge Base using the Bedrock Agent Runtime.

Model Context Protocol server for the Lacuna Music API — generate AI music from Claude Desktop, Claude Code, Cursor, Zed, and other MCP-compatible clients.

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

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

AI connector for Odoo ERP -- talk to your business data using natural language. Search, create, update, and manage records -- just ask.

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.

百度地图核心API现已全面兼容MCP协议，是国内首家兼容MCP协议的地图服务商。

The internet's largest queryable infrastructure graph — 7.39 billion nodes, 39 billion edges, 5.6 million threat-intelligence relationships. Pivot from any IP, domain, or ASN across DNS, BGP, WHOIS, GeoIP, and threat intel in a single Cypher query. Most threat-intel and OSINT APIs are point lookups: you ask about one indicator, you get one record. Investigations don't work that way — you start from one suspicious domain and need to trace its hosting, its sibling domains, its registrar, its email infrastructure, the ASN announcing its IP, and which feeds have flagged anything nearby. WhisperGraph stores all of that as a single connected graph and lets you traverse it natively. One query can answer questions that would take dozens of API calls anywhere else. Example questions you can ask **Is xyz-uknown@suspicious.com a safe email address? **What is the IP address of whisper.security? "What's the threat reputation of 45.142.213.55, and what feeds flagged it?" "Show every domain sharing this domain's MX records and registrant email." "Map the full DNS and BGP attack surface of example.com." "Which ASNs in Russia have the highest concentration of phishing-flagged IPs this quarter?" "Find all domains registered within 24 hours of suspicious-domain.com using the same registrar." "What's the historical WHOIS for disputed-domain.com going back 5 years?" "Which prefixes does AS13335 currently announce, and were any of them previously announced by a different ASN?"

A Model Context Protocol Server connector for Perplexity API, to enable web search without leaving the MCP ecosystem.

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

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

Yevideo is an AI creation platform for content marketing and creative teams, offering text-to-video, image-to-video, video-to-video, AI video editing, text-to-image, and image-to-image capabilities. Users can create, edit, and reuse assets in a unified workspace to shorten production cycles and improve publishing efficiency. The platform is suitable for social media operations, eCommerce assets, brand advertising, and creative testing, helping teams produce high-quality visual content quickly. For businesses that require continuous content updates, Yevideo lowers production barriers while increasing output and iteration speed.