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analyze

Turn event data into a finished product analytics report in one call: choose value event, detect aha moment, build funnel/retention, and write HTML.

Instructions

START HERE. Event data in, finished report out, one call.

Use this whenever the user asks anything general — "analyze my product", "how are my users doing", "find my aha moment". The other tools are parts; this is the whole thing. Only reach for them when the user asks for one specific number ("just show me retention").

It picks a value event, draws the dot plot, finds the aha moment, builds the funnel and retention curve, and writes the HTML report — then tells you what it found so you can say it out loud.

csv_path: a CSV with user_id, date, event (platform optional). No CSV yet? Call with no arguments and follow the instructions you get back — for a project with a database you will explore its schema and turn ordinary business tables (orders, sessions, posts) into events with load_from_db. Most early products have no events table; that is expected.

value_event: the action that means "this user got real value". Leave it out and the code picks the candidate most users repeat. Pass it yourself when you have read the codebase and know better — you can tell purchase from view_item and the code cannot. The result always names what was chosen and what else was available, so you can call again with a different one if the choice looks wrong.

lang: the user's language. en/ko/ja are built in; for any other language call get_report_strings, translate, and use generate_report directly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen
csv_pathNo
output_pathNodotplot_report.html
value_eventNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full load and does well: it enumerates the pipeline steps, states that the result names the chosen value event plus alternatives, and describes the empty-argument path that returns instructions. It does not disclose runtime/cost characteristics or the file-write side effect of output_path.

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?

Front-loaded with 'START HERE' and organized as short labeled blocks per parameter, so it scans well. It is on the long side and repeats the alternatives idea in two places, but nearly every sentence adds routing or fallback information.

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?

For a zero-required-argument orchestrator with no annotations and no output schema, the description covers inputs, the no-input path, and the shape of what comes back. Missing only output_path semantics and any hint about runtime or what the HTML report contains.

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 0%, so the description must compensate, and it does for three of four params: csv_path gets a required column list and a fallback behavior, value_event gets default logic and override rationale, and lang gets the built-in language list plus a workaround for unsupported languages. output_path is silently omitted.

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?

States a specific compound action — event data in, finished report out in one call — and explicitly contrasts itself with the sibling tools ('the other tools are parts; this is the whole thing'). An agent can tell instantly that this is the orchestrator rather than dot_plot, retention_curve, or generate_report.

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

Usage Guidelines5/5

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

Gives explicit when-to-use ('whenever the user asks anything general') and when-not with the alternative named ('only reach for them when the user asks for one specific number'). It even routes the no-CSV case to a follow-up flow, which is a rare level of routing detail.

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