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stickiness-series

Analyze user engagement by retrieving time-series data on return visit frequency. Choose weekly or monthly units to track stickiness trends over time using Hackle MCP server.

Instructions

Retrieves time-series data of user stickiness (return visit frequency). Available in weekly and monthly units.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoEnd date in YYYY-MM-DD format.
unitYes

Implementation Reference

  • src/index.ts:206-225 (registration)
    Registration of the 'stickiness-series' tool, including the handler function that fetches stickiness series data from the Hackle API endpoint using WebClient.get with unit and date query parameters.
    server.tool(
      'stickiness-series',
      'Retrieves time-series data of user stickiness (return visit frequency). Available in weekly and monthly units.',
      {
        unit: z.enum(['WEEK', 'MONTH']),
        date: z.string().optional().describe('End date in YYYY-MM-DD format.'),
      },
      async ({ unit = 'WEEK', date = '' }) => {
        const qs = stringify({ unit, date }, { addQueryPrefix: true });
    
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(await WebClient.get(`/api/v1/workspaces/auto-metrics/stickiness-series${qs}`)),
            },
          ],
        };
      },
    );
  • Handler function executing the tool logic: builds query string from inputs and retrieves JSON data from the stickiness-series API endpoint via WebClient.
    async ({ unit = 'WEEK', date = '' }) => {
      const qs = stringify({ unit, date }, { addQueryPrefix: true });
    
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(await WebClient.get(`/api/v1/workspaces/auto-metrics/stickiness-series${qs}`)),
          },
        ],
      };
    },
  • Input schema using Zod: unit enum ['WEEK', 'MONTH'] (default 'WEEK'), optional date string.
    {
      unit: z.enum(['WEEK', 'MONTH']),
      date: z.string().optional().describe('End date in YYYY-MM-DD format.'),
    },
  • WebClient.get static method used by the handler to perform HTTP GET request to the API.
    public static async get<T = unknown>(path: string, options?: Omit<RequestInit, 'method'>): Promise<T> {
      return this.request<T>('GET', path, options);
    }
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. It states the tool retrieves data, implying a read-only operation, but doesn't disclose behavioral traits such as authentication requirements, rate limits, data format, pagination, or error handling. For a data retrieval tool with no annotation coverage, this is a significant gap in transparency.

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 front-loaded and concise with two sentences that efficiently convey the tool's purpose and key parameter information. Every sentence earns its place by stating what the tool does and the available units, with no redundant or unnecessary details.

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 time-series data tool with no annotations, no output schema, and incomplete parameter documentation (50% coverage), the description is insufficient. It lacks details on return values, data structure, error conditions, and usage context, making it incomplete for effective agent operation.

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 50% (one parameter described, one not). The description adds value by explaining that the data is available in weekly and monthly units, which clarifies the 'unit' parameter's enum values. However, it doesn't address the 'date' parameter's semantics or provide additional context beyond what the schema partially covers, resulting in a baseline score.

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 tool retrieves time-series data of user stickiness (return visit frequency) with available weekly and monthly units. It specifies the verb 'retrieves' and resource 'time-series data of user stickiness', distinguishing it from siblings like active-user-series or retention-series by focusing on return visit frequency rather than active users or retention metrics. However, it doesn't explicitly contrast with all siblings, keeping it at 4 instead of 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 mentions available units but doesn't specify use cases, prerequisites, or comparisons with sibling tools like retention-series or analytics-chart-detail. This leaves the agent without contextual direction for tool 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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