predictfun-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GRAPH_API_KEY | Yes | Your API key from The Graph Subgraph Studio |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_platform_statsA | Get Predict.fun platform-wide stats: total volume, trades, open interest, yield, and active markets |
| get_top_marketsA | Get the top prediction markets ranked by volume, open interest, or trade count |
| get_market_detailsB | Get full details for a specific market/condition: volume, OI, resolution status, recent trades, top holders |
| get_market_orderbookA | Get the live order book for a Predict.fun market — current bids and asks with prices and sizes. Requires PREDICT_API_KEY. Provide market_id (integer from Predict.fun API) or condition_id. Use this to see the current state of the market, not historical trades. |
| get_trader_profileB | Get a trader's full profile: trading history, positions, P&L, and reward claims |
| get_recent_activityC | Get recent activity on Predict.fun: latest trades, splits, merges, redemptions, or yield events |
| get_yield_overviewA | Get Venus Protocol yield stats: deposits, redemptions, net balance, yield claims, and token mappings |
| get_whale_positionsB | Find the largest position holders across all Predict.fun markets |
| get_leaderboardA | Get the top traders on Predict.fun by volume, P&L, or trade count |
| get_resolved_marketsB | Get recently resolved markets with their outcomes and payout info |
| query_subgraphA | Run a custom GraphQL query against any Predict.fun subgraph. Use this for advanced queries not covered by other tools. |
| find_trader_personaA | Classify a trader into behavioral archetypes: whale_accumulator, yield_farmer, arbitrageur, early_mover, or resolution_sniper. Returns structured JSON with matched personas and supporting metrics. |
| scan_trader_personasB | Find traders matching a specific behavioral archetype across the platform. Returns structured JSON with matching traders and evidence. |
| tag_market_structureB | Classify a market by structural features: resolution latency, liquidity profile, oracle type, and tail-risk indicators. Returns structured JSON. |
| scan_markets_by_structureB | Find markets matching structural criteria: resolution speed, liquidity depth, oracle type, or tail-risk flags. Returns structured JSON. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| platform_overview | Get a full overview of the Predict.fun platform |
| analyze_trader | Analyze a specific trader's activity and P&L |
| market_deep_dive | Deep dive into a specific prediction market |
| yield_analysis | Analyze Predict.fun's Venus Protocol yield mechanics |
| whale_alert | Find the biggest players and their market positions |
| market_scanner | Scan for interesting markets — highest volume, most OI, recently resolved |
| custom_query_examples | Show example GraphQL queries for each Predict.fun subgraph |
| trader_persona_analysis | Classify traders by behavioral archetypes and find similar traders |
| market_quality_scan | Scan markets by structural quality indicators to find opportunities or risks |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.