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active-user-series

Track active user trends over time by retrieving time-series data in daily, weekly, or monthly intervals from Hackle-MCP. Analyze engagement patterns efficiently.

Instructions

Retrieves time-series data of active users. Available in daily, weekly, and monthly units.

Input Schema

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

Implementation Reference

  • The handler function for the 'active-user-series' tool. It takes unit and date parameters, builds a query string, calls WebClient.get to fetch time-series data from the specific API endpoint, stringifies the response as JSON, and returns it as text content.
      const qs = stringify({ unit, date }, { addQueryPrefix: true });
    
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(await WebClient.get(`/api/v1/workspaces/auto-metrics/active-user-series${qs}`)),
          },
        ],
      };
    },
  • Zod input schema defining parameters for the tool: 'unit' as enum ['DAY', 'WEEK', 'MONTH'] and optional 'date' string.
      unit: z.enum(['DAY', 'WEEK', 'MONTH']),
      date: z.string().optional().describe('End date in YYYY-MM-DD format.'),
    },
    async ({ unit = 'DAY', date = '' }) => {
  • src/index.ts:163-181 (registration)
    Registration of the 'active-user-series' tool using server.tool(), including name, description, input schema, and handler function.
      'active-user-series',
      'Retrieves time-series data of active users. Available in daily, weekly, and monthly units.',
      {
        unit: z.enum(['DAY', 'WEEK', 'MONTH']),
        date: z.string().optional().describe('End date in YYYY-MM-DD format.'),
      },
      async ({ unit = 'DAY', date = '' }) => {
        const qs = stringify({ unit, date }, { addQueryPrefix: true });
    
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(await WebClient.get(`/api/v1/workspaces/auto-metrics/active-user-series${qs}`)),
            },
          ],
        };
      },
    );
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool retrieves data (read operation) but doesn't mention permissions, rate limits, pagination, or response format. For a data retrieval tool with no annotation coverage, this leaves significant behavioral gaps. The description adds minimal value beyond the basic operation.

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 extremely concise with two sentences that directly state the tool's purpose and available units. Every word earns its place with zero waste. It's front-loaded with the core functionality. This is an excellent example of efficient communication.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a 2-parameter tool with no annotations and no output schema, the description provides basic purpose and parameter context but lacks details about response format, data structure, error conditions, or behavioral constraints. For a data retrieval tool, this leaves the agent with incomplete information about what to expect from the 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% (only 'date' parameter has description). The description mentions 'daily, weekly, and monthly units' which maps to the 'unit' parameter's enum values, adding some semantic context beyond the schema. However, it doesn't explain the 'date' parameter's purpose or format beyond what's in the schema. With partial schema coverage, the description provides moderate compensation.

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's purpose: 'Retrieves time-series data of active users' with specific resource (active users data) and verb (retrieves). It distinguishes from some siblings like 'retention-series' and 'stickiness-series' by specifying the metric type, but doesn't explicitly differentiate from all analytics tools. The purpose is clear but sibling differentiation could be more explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides some usage context by mentioning 'Available in daily, weekly, and monthly units,' which implies when to use different parameter values. However, it doesn't explicitly state when to choose this tool versus alternatives like 'retention-series' or 'stickiness-series,' nor does it mention prerequisites or exclusions. Usage is implied rather than explicitly guided.

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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