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therealsachin

Langfuse MCP Server

get_model_detail

Retrieve detailed information about a specific AI model's configuration, capabilities, and specifications to understand its technical parameters and operational characteristics.

Instructions

Get detailed information about a specific AI model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelIdYesThe model ID to retrieve detailed information for

Implementation Reference

  • The core handler function that executes the get_model_detail tool logic: fetches model details via Langfuse client and returns formatted JSON response or error.
    export async function getModelDetail(
      client: LangfuseAnalyticsClient,
      args: GetModelDetailArgs
    ) {
      try {
        const modelData = await client.getModel(args.modelId);
    
        return {
          content: [
            {
              type: 'text' as const,
              text: JSON.stringify(modelData, null, 2),
            },
          ],
        };
      } catch (error) {
        const errorMessage = error instanceof Error ? error.message : String(error);
        return {
          content: [
            {
              type: 'text' as const,
              text: `Error getting model detail: ${errorMessage}`,
            },
          ],
          isError: true,
        };
      }
    }
  • Zod schema defining input parameters (modelId) and inferred TypeScript type for the tool.
    export const getModelDetailSchema = z.object({
      modelId: z.string().describe('The model ID to retrieve detailed information for'),
    });
    
    export type GetModelDetailArgs = z.infer<typeof getModelDetailSchema>;
  • src/index.ts:565-578 (registration)
    Tool registration in the allTools array used by listToolsRequestHandler to expose the tool spec to MCP clients.
    {
      name: 'get_model_detail',
      description: 'Get detailed information about a specific AI model.',
      inputSchema: {
        type: 'object',
        properties: {
          modelId: {
            type: 'string',
            description: 'The model ID to retrieve detailed information for',
          },
        },
        required: ['modelId'],
      },
    },
  • src/index.ts:1077-1080 (registration)
    Dispatcher case in callToolRequestHandler that validates input with schema and invokes the handler function.
    case 'get_model_detail': {
      const args = getModelDetailSchema.parse(request.params.arguments);
      return await getModelDetail(this.client, args);
    }
  • Inclusion in readOnlyTools set, allowing the tool in readonly server mode.
    'get_model_detail',
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 detailed information but does not specify what 'detailed' includes, whether it's a read-only operation, any authentication requirements, rate limits, or error handling. This leaves significant gaps in understanding the tool's behavior.

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 front-loaded and appropriately sized for a simple tool, with no wasted information.

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

Completeness3/5

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

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It lacks details on what 'detailed information' entails, behavioral traits, or usage context, which could hinder an agent's ability to use it effectively without additional exploration.

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?

The input schema has 100% description coverage, clearly documenting the single required parameter 'modelId'. The description adds no additional semantic context beyond what the schema provides, such as examples of model IDs or format details, so it meets the baseline for high schema coverage.

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 tool's purpose with a specific verb ('Get') and resource ('detailed information about a specific AI model'), making it easy to understand what the tool does. However, it does not explicitly differentiate from sibling tools like 'list_models' or 'usage_by_model', which might provide related but different information.

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 sibling tools like 'list_models' for listing models or 'usage_by_model' for usage data, leaving the agent without context for tool selection in this server's ecosystem.

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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