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convalytics

by io.github.Dan-Cleary

events_count

Read-onlyIdempotent

Count CUSTOM PRODUCT events for a specific project in a time window, optionally filtered to one event name and/or one user. Custom events are emitted by explicit analytics.track() calls in app code (signup_completed, payment_succeeded, etc.). This does NOT count page views — use pageviews_count or weekly_digest for those. Returns count, unique visitors, and a truncated flag if the scan hit the maximum scan size.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional event name to filter by (e.g. 'signup_completed'). If omitted, counts all custom events in the window. Do NOT pass 'page_view' here — page views are in a separate table.
userNoOptional. Filter to one visitor/user. Accepts userEmail (case-insensitive) or visitorId (exact). For the full per-user snapshot prefer user_activity.
sinceNoStart of window as unix milliseconds. Defaults to 7 days ago.
untilNoEnd of window as unix milliseconds. Defaults to now.
projectYesProject name (case-insensitive, e.g. 'slopbench') or project id from list_projects.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive. Description goes beyond by specifying it returns count, unique visitors, and a truncated flag, and explains that custom events come from analytics.track(). No contradictions.

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?

Description is concise with three sentences, front-loading the core purpose and structure. Every sentence adds value without redundancy or fluff.

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

Completeness4/5

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

Given the tool has 5 parameters (all described) and no output schema, the description covers the main use, return fields, and usage constraints. It briefly mentions the truncated flag but could elaborate on maximum scan size. Still, it is sufficiently complete for an agent to understand and invoke the tool 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 baseline is 3. Description adds value by providing examples for 'name' (e.g., 'signup_completed'), clarifying not to pass 'page_view', and explaining 'user' accepts email or visitorId with a pointer to user_activity. This enriches understanding beyond schema.

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?

Description clearly identifies the tool as counting custom product events for a specific project in a time window, distinguishes from page views by explicitly stating it does not count them, and references alternative tools (pageviews_count, weekly_digest). It also lists return values (count, unique visitors, truncated flag).

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?

Description provides clear context for when to use (counting custom events) and when not to (page views), with alternatives. It also mentions optional filters and suggests user_activity for per-user snapshot. However, it does not explicitly differentiate from sibling tools like recent_events, which could also count events.

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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TDQS

A4/5.0
Disambiguation5/5

All tools have clearly distinct purposes. Funnel operations (compute, create, delete, get, list, update) are well-separated from event tools (events_count, recent_events) and pageview tools (pageviews_count, top_pages, top_referrers). Composite snapshots (user_activity, weekly_digest) and utility tools (get_usage, list_projects) are unique.

Naming Consistency3/5

Naming pattern is inconsistent: some tools use verb_noun (compute_funnel, create_funnel), others noun_verb (events_count, pageviews_count), and some are adjective_noun (recent_events, top_pages) or noun_noun (user_activity, weekly_digest). While readable, the lack of a consistent convention reduces predictability.

Tool Count4/5

15 tools is at the upper boundary of 'well-scoped' but still appropriate for an analytics platform. Each tool serves a necessary function without unnecessary duplication, though a few more could be justified.

Completeness4/5

The tool set covers core analytics operations: CRUD for funnels, event and pageview counting, top pages/referrers, user and weekly snapshots, and project/usage info. Minor gaps exist (e.g., no session-level detail), but overall it addresses common analytics queries well.

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