Skip to main content
Glama
kajirita2002

honeycomb-mcp-server

honeycomb_query_result_get

Retrieve specific query execution results by providing the dataset slug and query result ID in honeycomb-mcp-server for targeted data access.

Instructions

Get results of a specific query execution

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetSlugYesDataset slug the query result belongs to
queryResultIdYesQuery result ID to retrieve

Implementation Reference

  • MCP CallToolRequest handler case for 'honeycomb_query_result_get'. Extracts arguments (datasetSlug, queryResultId), calls HoneycombClient.getQueryResult, and returns JSON response.
    case "honeycomb_query_result_get": {
      const args = request.params
        .arguments as unknown as QueryResultGetArgs;
      if (!args.datasetSlug || !args.queryResultId) {
        throw new Error("datasetSlugとqueryResultIdが必要です");
      }
      const response = await client.getQueryResult(
        args.datasetSlug,
        args.queryResultId
      );
      return {
        content: [{ type: "text", text: JSON.stringify(response) }],
      };
    }
  • Tool definition object for 'honeycomb_query_result_get' including name, description, and inputSchema for validation (datasetSlug and queryResultId required).
    const queryResultGetTool: Tool = {
      name: "honeycomb_query_result_get",
      description: "Get query results for a previously executed query. The response body will be a JSON object with 'complete': true and the results populated once the query is complete.",
      inputSchema: {
        type: "object",
        properties: {
          datasetSlug: {
            type: "string",
            description: "The dataset slug or use `__all__` for endpoints that support environment-wide operations.",
          },
          queryResultId: {
            type: "string",
            description: "The unique identifier (ID) of the query result.",
          },
        },
        required: ["datasetSlug", "queryResultId"],
      },
    };
  • index.ts:791-792 (registration)
    Registration of the tool in the list returned by ListToolsRequest handler.
    queryResultCreateTool,
    queryResultGetTool,
  • HoneycombClient.getQueryResult method: Performs HTTP GET request to Honeycomb API endpoint /query_results/{datasetSlug}/{queryResultId} to fetch the query results, handles errors, returns JSON.
    async getQueryResult(datasetSlug: string, queryResultId: string): Promise<any> {
      const response = await fetch(
        `${this.baseUrl}/query_results/${datasetSlug}/${queryResultId}`,
        {
          method: "GET",
          headers: this.headers,
        }
      );
    
      if (!response.ok) {
        const errorBody = await response.text();
        console.error(`Query result error: Status=${response.status}, Body=${errorBody}`);
        throw new Error(`Failed to get query result: ${response.statusText}`);
      }
    
      return await response.json();
    }
  • TypeScript interface defining the input arguments for the honeycomb_query_result_get tool.
    interface QueryResultGetArgs {
      datasetSlug: string;
      queryResultId: string;
    }
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 results but doesn't describe what the results contain (e.g., data format, pagination), error conditions (e.g., invalid IDs), or performance aspects (e.g., latency, caching). This leaves significant gaps for an agent to understand 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, clear sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple retrieval tool, making it easy for an agent 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 the tool returns (e.g., query data, status, metadata), which is critical for a retrieval tool. The lack of behavioral context and output details makes it inadequate for an agent to use effectively without additional assumptions.

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 fully documents both parameters (datasetSlug and queryResultId). The description adds no additional meaning beyond what the schema provides, such as explaining how to obtain these IDs or their format constraints. 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 action ('Get results') and the target ('specific query execution'), which is a specific verb+resource combination. However, it doesn't distinguish this tool from sibling tools like 'honeycomb_query_get' or 'honeycomb_query_result_create', which handle query definitions or result creation respectively.

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., needing a query result ID from a previous execution), nor does it differentiate from related tools like 'honeycomb_query_get' for retrieving query definitions or 'honeycomb_query_result_create' for creating new results.

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

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/kajirita2002/honeycomb-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server