predictfun-mcp
Related Servers
Alternatives to predictfun-mcp
- AlicenseAqualityDmaintenanceAn MCP server that powers AI agents with indexed blockchain data from The Graph.3MIT
Related Servers
- AlicenseBqualityBmaintenanceAn MCP server that enables querying Polymarket prediction market data through The Graph subgraphs. It provides tools for accessing market stats, trader P\&L, user positions, and orderbook activity using AI agents.3577 npm4MIT
- AlicenseBqualityCmaintenanceMCP server to query Polymarket prediction market data via The Graph subgraphs and REST APIs, enabling AI agents to search markets, get live prices, order books, on-chain analytics, and trader profiles.3277 npmMIT
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables seamless integration with Polymarket, providing tools to search markets, fetch events, analyze leaderboards, query user activity, and more via MCP-compatible clients like Claude.22 npmMIT
- AlicenseNot gradedqualityCmaintenanceMCP server for querying and optionally trading across prediction markets (Polymarket, Kalshi, Limitless, Manifold) through a unified API.31MIT
- FlicenseAqualityBmaintenanceAI-agent ready FastMCP server for Polymarket market discovery, wallet analytics, and public CLOB data, providing a read-only interface for querying markets, wallets, and order books.22-
- AlicenseNot gradedqualityDmaintenanceReal-time prediction market intelligence for AI agents. Query Polymarket and Kalshi markets, wallet profiles, smart money leaderboards, social pulse signals, price candlesticks, and orderbook data — 13 agents, one MCP connection. Powered by 1.1TB+ of historical data.MIT
TDQS
Scored across 15 tools
Most tools have distinct purposes (e.g., get_market_orderbook vs get_market_details), but some overlap exists between get_leaderboard and get_whale_positions (both rank traders) and between find_trader_persona and scan_trader_personas (both deal with personas). Descriptions help differentiate, but slight ambiguity remains.
All names use snake_case and follow a verb_noun pattern, but verbs vary: get (9 tools), scan (2), find (1), tag (1), query (1). While each verb is semantically appropriate, the mix deviates from a strict pattern. Minor inconsistency.
15 tools is within the optimal range for a domain-specific server covering prediction markets and yield. Each tool serves a clear purpose without excessive redundancy, and the count feels well-scoped for the intended functionality.
The tool set covers core read operations: markets (details, top, resolved, orderbook), traders (profile, leaderboard, whale positions, personas), platform stats, and activity. The query_subgraph tool fills any gaps, making the surface effectively complete for a data-querying server.