@lodd/mcp-server

get_event_timeseries

Retrieve event count trends over time with optional filters for country, browser, OS, device, UTM source, or referrer to analyze frequency changes.

Instructions

Get event counts bucketed over time for a specific event. Useful for seeing how event frequency changes. Optional filters segment by country, browser, os, device type, UTM source, or referrer (matched against the session's page views).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "site": "<site>",
      "event_name": "<event_name>"
    },
    "name": "get_event_timeseries"
  }
}

Input Schema

Name	Required	Description	Default
`site`	Yes	Site domain or UUID
`period`	No	Time period	30d
`interval`	No	Bucket interval
`filter_os`	No	OS substring match (e.g. 'iOS')
`event_name`	Yes	The event name to get timeseries for
`filter_browser`	No	Browser substring match (e.g. 'Chrome')
`filter_country`	No	2-letter country code (e.g. 'US')
`filter_utm_source`	No	Exact UTM source (e.g. 'twitter')
`filter_device_type`	No	Device type: 'desktop' \| 'mobile' \| 'tablet'
`filter_referrer_contains`	No	Referrer substring (e.g. 'google')

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.15Jul 28, 2026

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations, the description carries full burden. It mentions that referrer filtering matches against session page views, but it omits details about output format (e.g., time buckets returned), ordering, pagination, error handling, or timezone interpretation.

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: two sentences that front-load the core purpose and immediately follow with filter options. Every word contributes meaning.

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 10 parameters, 2 required, and no output schema, the description is insufficiently complete. It does not describe the return structure (e.g., array of {timestamp, count}), bucket interval behavior, or default/edge cases. A more thorough description is needed for proper tool understanding.

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 coverage is 100%, so baseline is 3. The description adds minimal value by rephrasing filter capabilities and noting referrer matching context, but it does not clarify parameter interactions or format expectations beyond what schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get'), the resource ('event counts bucketed over time'), and the scoping ('for a specific event'). It distinguishes from siblings like get_event_counts (which likely returns total counts without time bucketing) and get_event_sequences.

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 indicates usefulness ('seeing how event frequency changes') and lists optional filters, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternative tools for non-time-series event data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.