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Skool MCP Server and API by Cotlet

Get leaderboard

get_leaderboard
Read-onlyIdempotent

The group's top 10 members by points for the last 7 days, 30 days or all time, and your rank. Returns the Leaderboards tab: the top 10 members by points earned in the period (one point per like received), with your own rank and points when you are on the board. Skool recounts it every few minutes; updated_at says when.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "group": "<group>"
    },
    "name": "get_leaderboard"
  }
}

Input Schema

NameRequiredDescriptionDefault
groupYesGroup URL name, e.g. 'yourgroup' for skool.com/yourgroup.
periodNo7d, 30d or all time. Default: 30d.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and non-open-world, so the safety profile is covered. The description adds genuinely new behavior: one point per like received, a recount every few minutes, and updated_at as a freshness signal. Minor gap: no indication of how stale a rank can be before update or what a missing rank looks like.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence is well front-loaded with scope and periods, but the second sentence largely repeats it ('top 10 members by points earned in the period') before adding only the like-based scoring and updated_at. Roughly one sentence of the two is redundant.

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 must describe the return, and it does: top 10 entries, your own rank and points only when you are on the board, plus updated_at. What is missing is the per-member entry shape (name, avatar, points fields), which an agent would need to consume the result reliably.

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% and the period enum is fully described in the schema, so baseline is 3. The description goes beyond it by defining what 'points' actually mean (one point per like received), which is the semantic an agent needs to interpret the period parameter; the group parameter gets no additional clarification.

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 verb and resource (get the group's leaderboard) plus its precise scope: top 10 members by points, three period options, and the caller's own rank. This clearly separates it from siblings like get_group_stats or list_members, which do not rank by points.

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?

The description maps the tool to the 'Leaderboards tab' and implies when it applies, but never explicitly says when to prefer it over get_group_stats (aggregate stats) or list_members. No exclusions or alternatives are named, so selection between the three is left 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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