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analytics-chart-list

Retrieve analytics chart lists for A/B test data with customizable filters like chart type, page size, and search keywords using Hackle API’s MCP server.

Instructions

fetch data analytics chart list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chartTypeNo
pageNumberNo
pageSizeNo
searchKeywordNo

Implementation Reference

  • Handler function that constructs query string from parameters and fetches the list of analytics charts from the API using WebClient.
    async ({ pageNumber = 1, pageSize = 100, searchKeyword = '', chartType }) => {
      const qs = stringify(
        {
          pageNumber,
          pageSize,
          searchKeyword,
          chartType,
        },
        { addQueryPrefix: true },
      );
    
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(await WebClient.get(`/api/v1/analytics/charts${qs}`)),
          },
        ],
      };
    },
  • Zod schema defining input parameters: pageNumber, pageSize, searchKeyword, and chartType.
    {
      pageNumber: z.number().optional().default(1),
      pageSize: z.number().optional().default(100),
      searchKeyword: z.string().optional(),
      chartType: z.enum(['FUNNEL', 'DATA_INSIGHT', 'RETENTION', 'USER_PATH']).optional(),
    },
  • src/index.ts:259-288 (registration)
    Tool registration call using McpServer.tool() method, including name, description, schema, and handler.
    server.tool(
      'analytics-chart-list',
      'fetch data analytics chart list.',
      {
        pageNumber: z.number().optional().default(1),
        pageSize: z.number().optional().default(100),
        searchKeyword: z.string().optional(),
        chartType: z.enum(['FUNNEL', 'DATA_INSIGHT', 'RETENTION', 'USER_PATH']).optional(),
      },
      async ({ pageNumber = 1, pageSize = 100, searchKeyword = '', chartType }) => {
        const qs = stringify(
          {
            pageNumber,
            pageSize,
            searchKeyword,
            chartType,
          },
          { addQueryPrefix: true },
        );
    
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(await WebClient.get(`/api/v1/analytics/charts${qs}`)),
            },
          ],
        };
      },
    );
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. 'fetch' implies a read operation, but the description doesn't mention authentication needs, rate limits, pagination behavior (despite pageNumber/pageSize parameters), or what the return format looks like. For a list tool with pagination parameters, this lack of behavioral context is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action and resource. However, its brevity contributes to underspecification rather than optimal clarity, as it omits necessary details for a tool with multiple parameters and sibling tools.

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 complexity (4 parameters with 0% schema coverage, no output schema, and no annotations), the description is incomplete. It doesn't address parameter meanings, return values, behavioral constraints, or differentiation from siblings. For a list tool with filtering and pagination capabilities, this minimal description leaves too many gaps for reliable agent use.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'chartType' enums represent, how 'searchKeyword' filters results, or the purpose of pagination parameters. With 4 parameters entirely undocumented in both schema and description, the agent lacks essential semantic context for effective tool invocation.

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 'fetch data analytics chart list' states the verb ('fetch') and resource ('data analytics chart list'), making the purpose clear at a basic level. However, it doesn't distinguish this tool from similar siblings like 'analytics-chart-detail' or 'data-report-list', leaving ambiguity about scope. The description is functional but lacks specificity about what kind of chart list this retrieves.

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. With siblings like 'analytics-chart-detail' (likely for single charts) and 'data-report-list' (potentially overlapping functionality), there's no indication of context, prerequisites, or exclusions. The agent must infer usage from the name alone, which is insufficient for reliable selection.

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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