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kajirita2002

honeycomb-mcp-server

honeycomb_datasets_list

Retrieve a comprehensive list of all datasets available in the environment using the MCP server for efficient data management and analysis.

Instructions

List all datasets in the environment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler implementation for the honeycomb_datasets_list tool. It calls the client's listDatasets method and returns the JSON-stringified response as tool output.
    case "honeycomb_datasets_list": {
      const response = await client.listDatasets();
      return {
        content: [{ type: "text", text: JSON.stringify(response) }],
      };
    }
  • Tool definition including name, description, and input schema (empty object since no parameters required).
    const datasetsListTool: Tool = {
      name: "honeycomb_datasets_list",
      description: "List all datasets in the environment. 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: {},
      },
    };
  • index.ts:782-798 (registration)
    Registration of all tools, including datasetsListTool, in the ListToolsRequest handler.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return {
        tools: [
          authTool,
          datasetsListTool,
          datasetGetTool,
          columnsListTool,
          queryCreateTool,
          queryGetTool,
          queryResultCreateTool,
          queryResultGetTool,
          datasetDefinitionsListTool,
          boardsListTool,
          boardGetTool,
        ],
      };
    });
  • HoneycombClient method that performs the actual API call to list datasets from https://api.honeycomb.io/1/datasets.
    async listDatasets(): Promise<any> {
      const response = await fetch(`${this.baseUrl}/datasets`, {
        method: "GET",
        headers: this.headers,
      });
    
      if (!response.ok) {
        throw new Error(`Failed to list datasets: ${response.statusText}`);
      }
    
      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 it's a list operation, implying read-only behavior, but doesn't cover critical aspects like pagination, rate limits, authentication requirements, or return format. For a tool with zero annotation coverage, this leaves significant gaps, scoring a 2.

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, clear sentence with zero waste—it directly states the action and resource without fluff. It's front-loaded and efficiently communicates the core purpose, earning a perfect score for conciseness.

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 tool's simplicity (0 parameters, no output schema), the description is minimal but adequate for basic understanding. However, with no annotations and no output schema, it lacks details on behavior (e.g., pagination, auth) and return values, which are important for a list operation. This incompleteness in a low-complexity context scores a 2.

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

Parameters4/5

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

The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for 0 parameters is 4, as the description doesn't need to compensate for any schema gaps. It appropriately avoids redundant parameter information.

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 ('List') and resource ('all datasets in the environment'), making the purpose unambiguous. It distinguishes from siblings like 'honeycomb_dataset_get' (singular) and 'honeycomb_datasets_create/update' (mutations), though it doesn't explicitly mention these distinctions. The description is specific but lacks explicit sibling differentiation, warranting a 4 rather than 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. It doesn't mention prerequisites (e.g., authentication), when not to use it, or compare it to similar tools like 'honeycomb_dataset_get' for single datasets. Without any usage context, it scores a 2.

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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