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Get Game Details

get_game_details

Retrieve detailed information about a specific video game from IGDB, including ratings, platforms, genres, release dates, and company involvement.

Instructions

Retrieve detailed information about a specific game from IGDB

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
game_idYesThe IGDB ID of the game
fieldsNoComma-separated list of fields to returnid,slug,name,rating,rating_count,hypes,first_release_date,platforms.name,genres.name,status,cover.url,summary,involved_companies.company.name,involved_companies.developer,involved_companies.publisher

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main asynchronous handler function that implements the get_game_details tool. It retrieves detailed game information from the IGDB API by game ID using a dynamically constructed query.
    async def get_game_details(
        game_id: Annotated[int, Field(description="The IGDB ID of the game")],
        ctx: Context,
        fields: Annotated[
            Optional[str],
            Field(description="Comma-separated list of fields to return"),
        ] = "id,slug,name,rating,rating_count,hypes,first_release_date,platforms.name,genres.name,status,cover.url,summary,involved_companies.company.name,involved_companies.developer,involved_companies.publisher",
    ) -> Dict[str, Any]:
        """
        Get detailed information about a specific game.
    
        Args:
            game_id: The IGDB ID of the game
            ctx: Context for accessing session configuration
            fields: Comma-separated list of fields to return (default: all fields)
    
        Returns:
            Detailed information about the game
        """
        igdb_client = get_igdb_client(ctx)
    
        query = f"fields {fields}; where id = {game_id};"
        results = await igdb_client.make_request("games", query)
    
        if not results:
            raise ValueError(f"No game found with ID {game_id}")
    
        return results[0]
  • The FastMCP @mcp.tool decorator that registers the get_game_details function as a tool, specifying its name, title, and description.
    @mcp.tool(
        name="get_game_details",
        title="Get Game Details",
        description="Retrieve detailed information about a specific game from IGDB"
    )
  • Input schema definition using Pydantic's Annotated and Field for parameter validation and documentation (game_id, ctx, fields). The default fields parameter specifies common game details to fetch.
        game_id: Annotated[int, Field(description="The IGDB ID of the game")],
        ctx: Context,
        fields: Annotated[
            Optional[str],
            Field(description="Comma-separated list of fields to return"),
        ] = "id,slug,name,rating,rating_count,hypes,first_release_date,platforms.name,genres.name,status,cover.url,summary,involved_companies.company.name,involved_companies.developer,involved_companies.publisher",
    ) -> Dict[str, Any]:
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information, implying a read-only operation, but does not cover aspects like rate limits, authentication needs, error handling, or response format. This is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with every part earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, 1 required), 100% schema coverage, and the presence of an output schema, the description is reasonably complete. It covers the core purpose, though it lacks behavioral details and usage guidelines, which are partially mitigated by the structured data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as examples or constraints for the 'fields' parameter. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Retrieve') and resource ('detailed information about a specific game from IGDB'), making the purpose evident. However, it does not explicitly differentiate from sibling tools like 'search_games' or 'get_most_anticipated_games', which might also retrieve game information, so it lacks sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention scenarios like needing details for a known game ID compared to searching by name or fetching anticipated games, leaving the agent without explicit usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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