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Funkel AI

get_analytics

Read-onlyIdempotent

Return product- or campaign-scoped activity in a UTC lookback interval. Completed actions and invitations use executed_at; connections and recorded replies use lead event timestamps. These are separate event counts, not one conversion cohort. Default 7 days, max 90.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoAggregation level. Default 'app'.
days_backNoLookback window in days (default 7)
campaign_idNoCampaign id (required when scope='campaign') (UUID format, e.g. 550e8400-e29b-41d4-a716-446655440000)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover the read-only, idempotent, non-destructive safety profile, so the description's job is to add semantics. It does so well, disclosing that completed actions/invitations key off executed_at while connections/replies use lead event timestamps, and that the numbers are separate counts rather than a single conversion cohort.

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?

Four tight sentences, front-loaded with what is returned, followed by the timestamp semantics and the window limits. Every sentence carries information; nothing is padded, though the timestamp sentence is dense.

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?

With no output schema, the description usefully characterizes the return as separate event counts and explains the timestamp basis, which is what an agent needs to interpret results. It stops short of describing the shape/pagination of the payload, but the semantic guidance is strong.

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 description coverage is 100%, so parameters are already documented (scope defaults to 'app', days_back default 7 / max 90, campaign_id required when scope='campaign'). The description only echoes the default/max window and the dual scope, adding little beyond the schema, so the baseline of 3 applies.

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?

States a specific verb (Return) and resource (product- or campaign-scoped activity counts) with a clear scoping dimension. It is distinguishable from most siblings, though it never names or contrasts with the nearby insight tools (get_insight, list_insights).

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?

Usage is only implied through the scope parameter ('product- or campaign-scoped'). There is no statement of when to reach for this tool versus get_insight/list_insights, and no exclusions or prerequisites are given, leaving selection to inference.

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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