Check which students trained today. Scans all active students and returns two lists: who_trained and who_didnt. Single call, no need to check one by one.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals that the tool scans all active students and returns two lists, and that it is a single call. Annotations already mark it as read-only and idempotent, so the description adds value by detailing the output format and efficiency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no waste. The core purpose is stated first, followed by output details and efficiency note. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool without an output schema, the description covers what the tool does and what it returns. However, it omits details about the account parameter and the exact structure of the lists, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one optional parameter (account) with 0% description coverage. The description does not mention this parameter or its purpose, leaving its effect ambiguous. For a low-coverage schema, the description should compensate but fails to do so.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks which students trained today, promising two lists (who_trained and who_didnt). This specific verb+resource distinguishes it from sibling tools, none of which focus on today's check-ins.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates the tool is a single call for today's check-ins and contrasts with checking one by one. It provides clear context for when to use it, though it does not explicitly exclude alternatives or name sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have identical descriptions but different names (e.g., mfit_client_get, mfit_client_list, mfit_client_list_groups all share the same description text). The flattened action pattern creates multiple tools for each action, making it extremely difficult for an agent to choose the correct one. Tools like mfit_workout_write_add_exercise and mfit_workout_write_archive_routine share the same verbose description, leading to high ambiguity.
The naming follows a loose mfit_<domain>_<action> pattern, but there is inconsistency: some tools use 'get' (mfit_get_client_count), others use 'list' (mfit_client_list), and actions like 'write' are overloaded with multiple sub-actions. The pattern is not uniform, and the flattened action suffix adds confusion.
With 48 tools, the count is excessive for what appears to be a single-domain server (personal training management). Many tools are redundant because they only differ by a single action parameter. The number could be reduced significantly by consolidating related operations.
The server covers a wide range of functionality: client management, workouts, exercises, files, finances, retention, and feedback. However, there are notable gaps such as direct messaging, advanced analytics, or payment processing. The duplication of tools also suggests that the actual feature set is less complete than the tool count implies.