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umami-mcp-server

by arttus
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TypeScript
Remote

Get custom events over time

umami_get_events_series
Read-onlyIdempotent

Count custom events over time, grouped by event name, to track conversions and interactions like form submissions, button clicks, and signups.

Instructions

Get counts of custom tracked events bucketed over time, grouped by event name.

Use this for conversion and interaction tracking: form submits, button clicks, signups, or any event fired through umami.track().

Args:

  • website (string, optional): Website ID, name, or domain.

  • range (string): Date range, default '7d'.

  • start_date / end_date (string, optional): Explicit bounds, overriding 'range'.

  • unit ('minute' | 'hour' | 'day' | 'month' | 'year', optional): Bucket size, chosen automatically if omitted.

  • event (string, optional): Restrict to a single event name.

  • filters (object, optional): Segment filters.

  • timezone (string, optional): IANA timezone.

  • response_format ('markdown' | 'json'): Output format (default: 'markdown').

Returns: JSON shape: { "website_id": string, "unit": string, "totals_by_event": { "": number }, "series": [ { "event": string, "timestamp": string, "count": number } ] }

Examples:

  • "How many contact form submits last week?" -> range="last_week", event="contact-form-submit"

  • "Which events fire most often?" -> range="30d"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
unitNoBucket size for the series. Omit to pick automatically from the range. Umami caps: minute up to 60 minutes, hour up to 30 days, day up to 6 months.
eventNoRestrict results to a single event name.
rangeNoDate range for the query, default '7d'. Relative ("30m", "24h", "7d", "4w", "3mo", "1y"), named ("today", "yesterday", "this_week", "last_week", "this_month", "last_month", "this_year", "last_year", "mtd", "ytd", "all_time"), or an explicit ISO date via start_date/end_date.
filtersNoOptional segment filters. Supported keys: path, referrer, title, query, browser, os, device, country, region, city, language, hostname, tag, event, distinctId, utmSource, utmMedium, utmCampaign, utmContent, utmTerm, segment, cohort. Example: { country: 'US', device: 'mobile' }.
websiteNoWebsite ID (UUID), name, or domain. Optional if UMAMI_DEFAULT_WEBSITE is set. Use umami_list_websites to discover values.
end_dateNoExplicit end date (YYYY-MM-DD, ISO 8601 timestamp, or epoch ms). Overrides 'range'.
timezoneNoIANA timezone for bucketing and day boundaries, e.g. 'America/New_York'. Defaults to UMAMI_TIMEZONE.
start_dateNoExplicit start date (YYYY-MM-DD, ISO 8601 timestamp, or epoch ms). Overrides 'range'.
response_formatNoOutput format: 'markdown' for a readable summary, 'json' for raw structured data.markdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral detail beyond that: results are grouped by event name, time-bucketed, unit selection is automatic when omitted, and the response is a specific JSON shape with totals and series.

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 well-structured with short labeled sections (overview, use case, args, returns, examples) and front-loads the key purpose. The Args section somewhat duplicates the schema, but the compact summaries and concrete examples justify the length.

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

Completeness5/5

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

Despite having no output schema, the description provides a full return shape, documents the default response format, covers all parameters via Args, and includes realistic examples. For a complex analytics read tool with nested filters and nine parameters, this is complete enough for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value beyond the schema by summarizing override behavior for start_date/end_date, explaining that unit is auto-selected, and providing natural-language examples mapping queries to parameter values like range='last_week' and event='contact-form-submit'.

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 opening sentence names a specific verb ('Get'), resource ('custom tracked events'), and core behavior ('bucketed over time, grouped by event name'). This clearly distinguishes it from pageviews-focused siblings and states exactly what the tool computes.

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

Usage Guidelines4/5

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

The description explicitly says to use this for conversion and interaction tracking, listing form submits, button clicks, signups, and umami.track() events. It gives clear context, though it does not explicitly name sibling tools to avoid, so it stops short of a full when-not/alternatives statement.

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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