mcp-server-proj MCP server

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

MCP server for semantic git queries. Walks the local `git` CLI plus optional GitHub API. Local-first; GH token only needed for PR/issue linkage. ## Why `git` answers these poorly: - "Who last touched this function?" — `git blame` only gives lines - "What PR introduced this line?" — manual blame → SHA → search GH - "Which files always change together?" — no built-in - "Show unmerged branches older than 30 days" — bash gymnastics - "What did I work on last week?" — manual log scrub LLM agents need this context to make safe edits. ## Tools | Tool | Purpose | |------|---------| | `who_touched` | Group blame by author. Lines, commits, last-touched, primary owner. | | `introducing_pr` | Find PR that introduced a line or commit. Merge-msg parse + GH API fallback. | | `co_change` | Files most often changed together with the input file. | | `branch_hygiene` | Branches w/ ahead/behind, last commit, merged status, stale flag. | | `recent_work` | Standup helper: author's commits + files + ins/del in a window. | | `commit_context` | Full commit context: subject, body, files, PR, related issues. | ## Install ```bash npm install -g git-insight-mcp claude mcp add --scope user git-insight -- git-insight-mcp

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.

高德地图官方 MCP Server

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

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.

Real-time prediction market + Hyperliquid perps intelligence for Claude. 22 tools combining Polymarket, HIP-4 on-chain markets, and Hyperliquid perpetuals — cross-platform signals, funding outliers, OI caps, liquidation clusters, whale activity, and HIP-4 vs Polymarket arb. Free tier (100 calls/day, no email required).

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

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.

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

Write notes to Flomo

Torify gives AI agents the Japanese locale primitives that standard libraries lack — imperial era date conversion (wareki), qualified invoice number validation with NTA registry lookup, corporate number lookup (法人番号), postal code resolution, name romanization (Hepburn), and kanji-to-kana conversion via Yahoo! JLP. 31 endpoints total. No authentication required for MCP. Pay-per-call $0.02/call via x402 USDC on Base, or $49/mo Pro subscription.

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

Inspect, manage, and kill local dev servers via MCP. Stop guessing what's on :3000. Five tools: list servers with framework detection, inspect ports, find zombies, diagnose conflicts, safe-kill with dry-run default. Local, no cloud, no telemetry.

Vocametrix is an MCP server that brings clinical-grade voice analysis into any MCP-compatible client (Claude Desktop, Cursor, Zed, Windsurf). It exposes more than 40 endpoints covering AVQI, DSI, CPP, HNR, jitter/shimmer, formants F1-F4, GNE, pronunciation assessment in 30+ languages, speech-to-text, phoneme detection, prosody similarity, and eGeMAPS. For speech-language pathologists, voice researchers, and healthtech developers. Requires a Vocametrix API key (free trial at vocametrix.com/registration).

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

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.

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

MuntuAI is an outbound for AI agents. Your agent can now launch and manage large scale personalized email campaigns with its own email accounts.

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。

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?"

Web content fetching and conversion for efficient LLM usage

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.

28 WhoisXML API tools for AI agents — WHOIS lookups & history, reverse WHOIS, DNS lookup & history, reverse DNS/MX/NS, IP geolocation, IP netblocks, SSL certificates, subdomain discovery, threat intelligence, domain categorization, email verification, typosquatting detection, and bulk variants for batched lookups. Stdio and Streamable-HTTP transports, OAuth 2.1 with PKCE for hosted mode, npm and Docker distributions.

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