Get all unread workout feedbacks from students. Each feedback includes: student name, Borg RPE score (1-10 perceived effort), text feedback, workout date/time, duration. Shows which students completed workouts and how they felt.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and destructiveHint, indicating a safe read operation. The description adds detail on the data returned (student name, RPE, etc.) and the scope (unread feedbacks), enhancing transparency without contradiction.
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?
Two concise sentences that front-load the main purpose and then list included fields. No wasted words.
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?
Describes the output data well, but fails to address the input parameter. For a tool with only one parameter and no output schema, this is a notable gap.
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 single parameter 'account' has 0% schema description coverage. The description does not explain its purpose, meaning, or required format, leaving the agent to guess.
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?
Clearly states the verb 'Get' and resource 'unread workout feedbacks from students', differentiating from sibling tools like mfit_get_client_list or mfit_get_client_workouts. Specifies the content of each feedback.
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?
Implies usage for retrieving unread feedbacks, but provides no explicit guidance on when to use this tool versus alternatives (e.g., other 'get' tools) or when not to use it.
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.