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honeycomb-mcp-server

honeycomb_query_get

Retrieve specific query details by providing the dataset slug and query ID from the honeycomb-mcp-server. Simplify query information access for targeted datasets.

Instructions

Get information about a specific query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetSlugYesDataset slug the query belongs to
queryIdYesQuery ID to retrieve

Implementation Reference

  • Defines the Tool object for 'honeycomb_query_get' including name, description, and input schema requiring datasetSlug and queryId.
    const queryGetTool: Tool = {
      name: "honeycomb_query_get",
      description: "Get information about a specific query. Returns the query specification, not the query results.",
      inputSchema: {
        type: "object",
        properties: {
          datasetSlug: {
            type: "string",
            description: "The dataset slug or use `__all__` for endpoints that support environment-wide operations.",
          },
          queryId: {
            type: "string",
            description: "The unique identifier (ID) of the query.",
          },
        },
        required: ["datasetSlug", "queryId"],
      },
    };
  • MCP tool handler case that validates arguments, calls HoneycombClient.getQuerySpec, and returns the JSON response.
    case "honeycomb_query_get": {
      const args = request.params.arguments as unknown as QueryGetArgs;
      if (!args.datasetSlug || !args.queryId) {
        throw new Error("datasetSlug and queryId are required");
      }
      const response = await client.getQuerySpec(
        args.datasetSlug,
        args.queryId
      );
      return {
        content: [{ type: "text", text: JSON.stringify(response) }],
      };
    }
  • HoneycombClient method that performs the actual HTTP GET request to retrieve the query specification from the Honeycomb API.
    async getQuerySpec(datasetSlug: string, queryId: string): Promise<any> {
      const response = await fetch(`${this.baseUrl}/queries/${datasetSlug}/${queryId}`, {
        method: "GET",
        headers: this.headers,
      });
    
      if (!response.ok) {
        throw new Error(`Failed to get query spec: ${response.statusText}`);
      }
    
      return await response.json();
    }
  • index.ts:784-796 (registration)
    Registers the queryGetTool in the list of available tools returned by ListToolsRequestHandler.
    tools: [
      authTool,
      datasetsListTool,
      datasetGetTool,
      columnsListTool,
      queryCreateTool,
      queryGetTool,
      queryResultCreateTool,
      queryResultGetTool,
      datasetDefinitionsListTool,
      boardsListTool,
      boardGetTool,
    ],
  • TypeScript interface defining the input arguments for the honeycomb_query_get tool.
    interface QueryGetArgs {
      datasetSlug: string;
      queryId: string;
    }
Behavior2/5

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

With no annotations provided, the description carries full burden but only states it 'gets information,' which is minimal. It doesn't disclose behavioral traits like whether this is a read-only operation (implied but not explicit), authentication needs, rate limits, error handling, or what format the information is returned in. This leaves significant gaps for an agent to understand how to use it effectively.

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 purpose without fluff. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every word earns its place, adhering to best practices 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 complexity of a query retrieval tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'information' is returned (e.g., JSON structure, fields), potential side effects, or error conditions. For a tool that likely returns detailed data, this lack of context makes it inadequate for an agent to use confidently.

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%, with both parameters (datasetSlug, queryId) well-documented in the schema. The description adds no additional meaning beyond the schema, such as explaining parameter relationships or examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but no extra value is provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get information about a specific query' clearly states the action (get) and resource (query), but it's vague about what 'information' includes (e.g., metadata, results, configuration) and doesn't differentiate from sibling tools like honeycomb_query_result_get, which might retrieve query execution results. It avoids tautology but lacks specificity.

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?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't explain if this is for retrieving saved query definitions versus live results, or how it differs from honeycomb_query_result_get. The description implies usage for a 'specific query' but offers no context on prerequisites or exclusions.

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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