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retention-series

Retrieve time-series user retention data in daily, weekly, or monthly units using the specified end date in YYYY-MM-DD format. Analyze retention trends for informed decision-making.

Instructions

Retrieves time-series data of user retention. 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 that takes unit and date parameters, builds a query string, calls WebClient.get to fetch retention series data from the '/api/v1/workspaces/auto-metrics/retention-series' endpoint, stringifies the JSON response, and returns it as text content.
    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/retention-series${qs}`)),
          },
        ],
      };
    },
  • Zod input schema defining the parameters: unit (required enum: DAY, WEEK, MONTH, default DAY) and optional date string.
    {
      unit: z.enum(['DAY', 'WEEK', 'MONTH']),
      date: z.string().optional().describe('End date in YYYY-MM-DD format.'),
    },
  • src/index.ts:184-203 (registration)
    The server.tool registration call that defines and registers the 'retention-series' tool with its description, input schema, and handler function.
    server.tool(
      'retention-series',
      'Retrieves time-series data of user retention. 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/retention-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 the full burden of behavioral disclosure. It states the tool 'retrieves' data, implying a read-only operation, but doesn't address critical aspects like authentication requirements, rate limits, data freshness, or response format. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 appropriately concise with two sentences that directly state the tool's purpose and parameter options. It's front-loaded with the core functionality. There's no wasted language, though it could be slightly more structured by explicitly listing parameters.

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 (retrieving analytics data), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral traits, response format, error handling, or usage context relative to siblings. For a tool with two parameters and significant operational implications, this minimal description fails to provide adequate context for reliable agent use.

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 of two parameters has a description). The description adds value by explaining the 'unit' parameter options ('daily, weekly, and monthly units'), which aligns with the enum in the schema. However, it doesn't clarify the 'date' parameter beyond what the schema provides ('End date in YYYY-MM-DD format'), nor does it explain the interaction between parameters (e.g., if 'date' is optional, what default is used). The baseline is 3 due to moderate schema coverage and some added meaning.

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 user retention.' It specifies the verb ('retrieves'), resource ('time-series data of user retention'), and available granularities ('daily, weekly, and monthly units'). However, it doesn't explicitly differentiate from sibling tools like 'active-user-series' or 'stickiness-series' that might also retrieve time-series data, which prevents a perfect score.

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 exclusions. With sibling tools like 'analytics-chart-list' and 'data-report-list' that might offer similar data, the lack of comparative context leaves the agent without clear selection criteria.

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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