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Langfuse MCP Server

get_dataset_item

Retrieve detailed information about a specific dataset item by its ID to analyze Langfuse analytics, cost metrics, and usage data.

Instructions

Get detailed information about a specific dataset item.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemIdYesID of the dataset item to retrieve

Implementation Reference

  • The primary handler function for the 'get_dataset_item' tool. It takes a Langfuse client and arguments, fetches the dataset item by ID, and returns formatted content or error.
    export async function getDatasetItem(
      client: LangfuseAnalyticsClient,
      args: GetDatasetItemArgs
    ) {
      try {
        const data = await client.getDatasetItem(args.itemId);
        return {
          content: [{ type: 'text' as const, text: JSON.stringify(data, null, 2) }],
        };
      } catch (error) {
        const errorMessage = error instanceof Error ? error.message : String(error);
        return {
          content: [{ type: 'text' as const, text: `Error: ${errorMessage}` }],
          isError: true,
        };
      }
    }
  • Zod schema for validating input arguments to the get_dataset_item tool, requiring a non-empty itemId string.
    export const getDatasetItemSchema = z.object({
      itemId: z.string().min(1).describe('ID of the dataset item to retrieve'),
    });
  • src/index.ts:1118-1121 (registration)
    Switch case in the CallToolRequestSchema handler that parses arguments using the schema and dispatches to the getDatasetItem handler function.
    case 'get_dataset_item': {
      const args = getDatasetItemSchema.parse(request.params.arguments);
      return await getDatasetItem(this.client, args);
    }
  • src/index.ts:744-757 (registration)
    Tool registration entry in the listTools handler, defining name, description, and input schema for get_dataset_item.
    {
      name: 'get_dataset_item',
      description: 'Get detailed information about a specific dataset item.',
      inputSchema: {
        type: 'object',
        properties: {
          itemId: {
            type: 'string',
            description: 'ID of the dataset item to retrieve',
          },
        },
        required: ['itemId'],
      },
    },
  • LangfuseAnalyticsClient method that makes the actual API call to retrieve a dataset item by ID, used by the tool handler.
    async getDatasetItem(itemId: string): Promise<any> {
      const authHeader = 'Basic ' + Buffer.from(
        `${this.config.publicKey}:${this.config.secretKey}`
      ).toString('base64');
    
      const response = await fetch(`${this.config.baseUrl}/api/public/dataset-items/${encodeURIComponent(itemId)}`, {
        headers: {
          'Authorization': authHeader,
        },
      });
    
      if (!response.ok) {
        await this.handleApiError(response, 'Get Dataset Item');
      }
    
      return await response.json();
    }
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 this is a read operation ('Get'), implying it's non-destructive, but doesn't mention any behavioral traits like error handling, permissions required, rate limits, or what 'detailed information' includes. For a tool with zero annotation coverage, this is inadequate.

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 front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, resource, and scope.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' includes (e.g., structure, fields), error cases, or how this tool differs from siblings. For a tool with no structured behavioral or output data, the description should provide more context to be fully helpful.

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 schema description coverage is 100%, with the single parameter 'itemId' fully documented in the schema. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain what an 'itemId' is or where to find it). 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 tool's purpose with a specific verb ('Get') and resource ('dataset item'), and specifies the scope ('detailed information about a specific dataset item'). However, it doesn't explicitly differentiate from sibling tools like 'get_dataset' or 'list_dataset_items', which would require a 5.

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 like 'get_dataset' (for dataset-level info) or 'list_dataset_items' (for listing items). It lacks any context about prerequisites, timing, or exclusions, leaving the agent to infer usage from the name alone.

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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