get_most_anticipated_games
Retrieve upcoming video games with high anticipation by fetching titles filtered for future releases and sorted by community hype count.
Instructions
Fetch upcoming games sorted by hype count, filtered for future or TBA releases
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| fields | No | Comma-separated list of fields to return | id,slug,name,hypes,first_release_date,platforms.name,genres.name,status |
| limit | No | Maximum number of results to return | |
| min_hypes | No | Minimum number of hypes required |
Implementation Reference
- src/igdb_mcp_server/server.py:222-270 (handler)The @mcp.tool decorator registers the tool and defines its input schema via parameter annotations. The async function implements the core logic: fetches IGDB client, builds Apicalypse query filtering for future/TBA games with minimum hypes, sorts by hypes descending, and executes the API request.@mcp.tool( name="get_most_anticipated_games", title="Get Most Anticipated Games", description="Fetch upcoming games sorted by hype count, filtered for future or TBA releases" ) async def get_most_anticipated_games( ctx: Context, fields: Annotated[ str, Field(description="Comma-separated list of fields to return"), ] = "id,slug,name,hypes,first_release_date,platforms.name,genres.name,status", limit: Annotated[ int, Field(description="Maximum number of results to return", ge=1, le=500) ] = 25, min_hypes: Annotated[ int, Field(description="Minimum number of hypes required", ge=0) ] = 25, ) -> List[Dict[str, Any]]: """ Get the most anticipated upcoming games based on hype count. Automatically filters for future or TBA releases. Args: ctx: Context for accessing session configuration fields: Comma-separated list of fields to return limit: Maximum number of results to return (default: 25, max: 500) min_hypes: Minimum number of hypes required (default: 25) Returns: List of most anticipated games sorted by hype count """ igdb_client = get_igdb_client(ctx) # Get current timestamp current_timestamp = int(time.time()) # Build query: games with hypes that are either future releases or TBA query = ( f"fields {fields}; " f"where hypes >= {min_hypes} & " f"(status = null | status != 0) & " f"(first_release_date > {current_timestamp} | first_release_date = null); " f"sort hypes desc; " f"limit {limit};" ) return await igdb_client.make_request("games", query)
- MCP prompt template 'most_anticipated' that guides the model on how to use the get_most_anticipated_games tool effectively, including specific fields, formatting, and analysis instructions.@mcp.prompt( name="most_anticipated", title="Most Anticipated Games", description="Find the most anticipated upcoming games based on user hypes on IGDB" ) def most_anticipated() -> str: """Finds the most anticipated upcoming games based on user hypes on IGDB.""" return """Top most anticipated upcoming games: 1. Use the get_most_anticipated_games tool: - Required fields: id,slug,name,hypes,first_release_date,platforms.name,genres.name,involved_companies.company.name,involved_companies.developer,involved_companies.publisher,status - Limit: 10 2. Format results as: ## 🎮 [Name] ([first_release_date]) [Link](https://www.igdb.com/games/[slug]) • **Hypes**: [hypes] • **Platforms**: [comma-separated] • **Genres**: [comma-separated] • **Developer**: [developer names or "Unknown"] 3. Include statistics: • Hype analysis (average, highest, median) • Platform/genre distribution • Release timeline breakdown (next 3 months / 6 months / 1 year / TBA) • Development status breakdown if available The tool automatically filters for future releases or TBA games with significant hype."""