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nks-hub

rybbit-mcp

by nks-hub

Event Time Series

rybbit_get_event_timeseries
Read-onlyIdempotent

Retrieve event counts as time-series data with configurable time buckets to analyze trends over specific periods for a website.

Instructions

Get custom event counts as time-series data with configurable buckets. Useful for analyzing event trends over time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteIdYesSite ID (numeric ID or domain identifier)
startDateNoStart date in ISO format (YYYY-MM-DD)
endDateNoEnd date in ISO format (YYYY-MM-DD)
timeZoneNoIANA timezone (e.g., Europe/Prague). Default: UTC
filtersNoArray of filters. Example: [{parameter:'browser',type:'equals',value:['Chrome']},{parameter:'country',type:'equals',value:['US','DE']}]
pastMinutesStartNoAlternative to dates: minutes ago start (e.g., 60 = last hour)
pastMinutesEndNoAlternative to dates: minutes ago end (default 0 = now)
bucketNoTime bucket granularity (default: day). Use 'hour' for last 24h, 'week'/'month' for long ranges

Implementation Reference

  • The registration and handler logic for the rybbit_get_event_timeseries tool.
    server.registerTool(
      "rybbit_get_event_timeseries",
      {
        title: "Event Time Series",
        annotations: { readOnlyHint: true, idempotentHint: true, openWorldHint: true, destructiveHint: false },
        description:
          "Get custom event counts as time-series data with configurable buckets. Useful for analyzing event trends over time.",
        inputSchema: {
          ...analyticsInputSchema,
          bucket: bucketSchema,
        },
      },
      async (args) => {
        try {
          const params = client.buildAnalyticsParams(args);
          const data = await client.get(`/sites/${args.siteId}/events/bucketed`, params);
          return {
            content: [{ type: "text" as const, text: truncateResponse(data) }],
          };
        } catch (err) {
          const message = err instanceof Error ? err.message : String(err);
          return {
            content: [{ type: "text" as const, text: `Error: ${message}` }],
            isError: true,
          };
        }
      }
    );
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety profiles. The description adds minimal behavioral context beyond this, though 'custom event counts' hints at the aggregation nature. It does not address rate limits, empty result handling, or the specific time-series data structure returned.

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 consists of two efficient sentences with zero waste. The first sentence front-loads the core functionality (getting time-series event data), while the second provides the use case context.

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 8 parameters with alternative date input methods (pastMinutes vs. dates) and complex filtering capabilities, the description is minimal but adequate. It mentions 'time-series data' compensating slightly for the missing output schema, though it could clarify the return format or provide an example of the aggregation result.

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?

With 100% schema description coverage, the baseline is 3. The description references 'configurable buckets' (the bucket parameter) but does not add semantic meaning, syntax clarification, or usage guidance beyond what the schema already provides for the 8 parameters including complex filters.

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 'custom event counts as time-series data with configurable buckets,' specifying the verb, resource, and format. However, it does not explicitly differentiate from sibling tools like 'rybbit_get_overview_timeseries' or 'rybbit_get_user_event_breakdown'.

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 only provides a generic use case ('Useful for analyzing event trends over time') without specifying when to use this tool versus alternatives like 'rybbit_get_metric' or the other timeseries siblings. No prerequisites or exclusions are mentioned.

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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