CoachingPortal by Cotlet

Set workspace units in CoachingPortal

coachingportal_set_workspace_units

Read-onlyIdempotent

Set workspace units in the user's own CoachingPortal account from ChatGPT, Claude or any AI agent. Returns how to do it in CoachingPortal step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's CoachingPortal Cloud Browser. CoachingPortal's official API: CoachingPortal publishes no public API.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "coachingportal_set_workspace_units"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Beyond the readOnly/idempotent/non-destructive annotations, it explains the apparent paradox of a 'set' tool that is read-only: it opens nothing by itself and only returns step-by-step navigation instructions, with the actual change done through cotlet_browser_* tools. It also notes CoachingPortal has no public API, explaining why a browser path is required. It doesn't describe the shape of the returned instructions or failure conditions.

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 key behavioral point (returns instructions, opens nothing) is front-loaded in the second sentence, which is good. However, filler like 'from ChatGPT, Claude or any AI agent' and the awkwardly repeated 'CoachingPortal's official API: CoachingPortal publishes no public API' add bulk without proportional 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?

With zero parameters, no output schema and annotations already covering the safety profile, the description supplies the one thing an agent actually needs: that this is an instruction-generating step to be followed by cotlet_browser_* calls. Minor gaps remain around what the returned steps look like and any browser-session prerequisite.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. The description correctly implies the caller supplies nothing beyond the session context.

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?

The description names a specific resource and operation ('Set workspace units in the user's own CoachingPortal account') and immediately self-corrects the name's implication by stating it 'Returns how to do it in CoachingPortal step by step... and opens nothing by itself.' That makes the guide-only nature clear, though the imperative verb 'Set' still initially reads as a mutation and no sibling tool is named for contrast.

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?

It gives explicit usage context: call it from ChatGPT, Claude or any AI agent, then carry out the change with the cotlet_browser_* tools in the user's CoachingPortal Cloud Browser. That hand-off is a concrete precondition rather than vague inference. It stops short of when-not-to-use guidance or comparison to sibling setters like coachingportal_update_workspace_branding.

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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