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Unfold It MCP Server

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by Unfold-it

get_analytics

Retrieve aggregated analytics for organizational goals: cohort KPIs, at-risk learners, completion funnels, and resource engagement. Filter by metadata and group by dimensions like track or cohort.

Instructions

Get aggregated analytics for your organization's goals.

Returns cohort KPIs (total, active, completed, blocked), claim metrics (activation rate, time-to-claim), at-risk learners (inactive for N days), an optional step-level completion funnel, and optional resource engagement breakdown.

Use groupBy to break down completion rates by a metadata dimension (e.g. groupBy="track" shows completion rates for frontend, backend, etc).

Use the metadata filter to narrow the scope to a specific cohort or department (e.g. metadata={cohort: "spring-2026"}).

Examples:

  • "How is the spring 2026 cohort doing?" -> get_analytics({ metadata: { cohort: "spring-2026" } })

  • "Which track has the highest completion rate?" -> get_analytics({ groupBy: "track" })

  • "How many students are at risk this week?" -> get_analytics({ inactiveDays: 7 })

  • "Show me the step-by-step funnel for the frontend track" -> get_analytics({ metadata: { track: "frontend" }, includeFunnel: true })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
date_toNoISO date (YYYY-MM-DD). Only include goals created on or before this date.
group_byNoMetadata key to group completion rates by (e.g. 'track', 'cohort', 'department'). Returns a completionByDimension breakdown.
metadataNoFilter to a specific cohort or segment by metadata tags (e.g. {cohort: 'spring-2026', track: 'frontend'}).
date_fromNoISO date (YYYY-MM-DD). Only include goals created on or after this date.
inactive_daysNoGoals with no step activity in this many days are flagged as at-risk. Default: 7.
include_funnelNoInclude the step-by-step completion funnel showing where learners drop off.
include_resourcesNoInclude resource engagement breakdown (video vs article, AI vs user-added).
Behavior3/5

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

No annotations were provided, so the description bears full responsibility. It implies a read operation but does not explicitly state read-only nature or disclose any side effects, impact on data, or performance considerations.

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 bullet points and examples, making it easy to scan. It is relatively concise given the amount of information covered, though some repetition could be trimmed.

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 no output schema, the description thoroughly explains return values (KPIs, at-risk learners, funnels, resource engagement). Given 7 parameters and nested objects, it provides sufficient context for using the tool.

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 description coverage is 100%; the description adds value by providing examples for groupBy, metadata, and other parameters, clarifying their use in context beyond the schema definitions.

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 tool gets aggregated analytics for goals, listing specific KPIs and breakdowns. It distinguishes from siblings like get_goal_status and list_goals by emphasizing aggregation and cohort metrics.

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 provides examples and explains when to use groupBy and metadata filters, giving clear usage context. However, it does not explicitly state when not to use this tool or contrast it with alternative siblings.

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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