list_subjectsList the subjects tutors teach, with the search terms and synonyms that match each. Use before search_tutors to pick a good subject term.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description primarily adds the detail that results include search terms and synonyms. No additional behavioral context like pagination or latency is provided, but with zero parameters and safe nature, it's adequate.
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. The first states the function, the second provides usage context. No redundant information. Every sentence serves a purpose.
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?
Given no parameters, no output schema, and comprehensive annotations, the description fully covers the tool's purpose and usage context. It specifies the output includes synonyms, which is valuable for the agent.
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?
There are no parameters; schema coverage is 100%. The description adds value by explaining the output content (subjects with synonyms), which is more than the baseline of 3 for no parameters.
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 'list', the resource 'subjects tutors teach', and additional details 'with the search terms and synonyms that match each'. Distinguishes from sibling tools like search_tutors and get_tutor.
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?
Explicitly advises using this tool before search_tutors to pick a good subject term, providing clear context for when to use it. Does not mention alternatives or when not to use, but the guidance is specific and helpful.
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.
Each tool addresses a distinct task: info, search, profile detail, reviews, subjects, self-serve booking prep, and human-assisted requests. Even the two booking-related tools are clearly separated by their descriptions and outcomes.
All tools follow a consistent lowercase snake_case verb_noun pattern such as get_tutor, list_subjects, search_tutors, prepare_booking, and request_human_tutor. The naming makes the function of each tool predictable.
Seven tools is a well-scoped set for a tutoring platform integration. Each tool addresses a meaningful part of the user journey without redundancy or bloat.
The surface covers platform info, discovery, evaluation, subject lookup, and both self-serve and human-assisted booking paths. A minor gap is the absence of direct booking management or tutor availability checks, but these are not required for the primary workflow.