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kajirita2002

honeycomb-mcp-server

honeycomb_dataset_get

Retrieve detailed information about a specific dataset by providing its unique slug using the MCP server tool designed for dataset queries.

Instructions

Get information about a specific dataset

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetSlugYesDataset slug to retrieve

Implementation Reference

  • Handler case in the tool request switch that extracts datasetSlug argument and invokes client.getDataset to retrieve dataset information.
    case "honeycomb_dataset_get": {
      const args = request.params.arguments as unknown as DatasetGetArgs;
      if (!args.datasetSlug) {
        throw new Error("datasetSlug is required");
      }
      const response = await client.getDataset(args.datasetSlug);
      return {
        content: [{ type: "text", text: JSON.stringify(response) }],
      };
    }
  • Tool definition object containing the name, description, and inputSchema for parameter validation (requires datasetSlug).
    const datasetGetTool: Tool = {
      name: "honeycomb_dataset_get",
      description: "Get information about a specific dataset. A Dataset represents a collection of related events that come from the same source, or are related to the same source.",
      inputSchema: {
        type: "object",
        properties: {
          datasetSlug: {
            type: "string",
            description: "The dataset slug.",
          },
        },
        required: ["datasetSlug"],
      },
    };
  • index.ts:786-787 (registration)
    Registration of the datasetGetTool in the tools array returned by ListToolsRequestHandler.
    datasetsListTool,
    datasetGetTool,
  • HoneycombClient.getDataset method: performs HTTP GET to /datasets/{slug} endpoint to fetch dataset details.
    async getDataset(slug: string): Promise<any> {
      const response = await fetch(`${this.baseUrl}/datasets/${slug}`, {
        method: "GET",
        headers: this.headers,
      });
    
      if (!response.ok) {
        throw new Error(`Failed to get dataset: ${response.statusText}`);
      }
    
      return await response.json();
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Get information' which implies a read-only operation, but it doesn't clarify permissions, rate limits, error handling, or what 'information' includes (e.g., metadata, structure). This leaves significant gaps 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 with zero waste. It's front-loaded and appropriately sized for a simple tool, making it easy to parse quickly.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'information' is returned, potential errors, or behavioral traits like idempotency. For a tool with minimal structured data, this leaves the agent with insufficient context to use it effectively.

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 description adds no parameter semantics beyond the input schema, which has 100% coverage and fully documents the single parameter 'datasetSlug'. Since schema coverage is high, the baseline score is 3, as the description doesn't need to compensate but also doesn't add extra value.

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 ('Get') and resource ('information about a specific dataset'), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'honeycomb_board_get' or 'honeycomb_marker_get', which follow the same pattern, so it lacks sibling differentiation.

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 doesn't mention related tools like 'honeycomb_datasets_list' for listing datasets or specify prerequisites, leaving usage context implied at best.

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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