check_availabilityCheca vagas restantes na PRÓXIMA turma de um curso. Mescla matrículas reais no Supabase com a curva de marketing (computeEffectiveSlots) e nunca mostra mais vagas do que realmente existem. Se soldOut=true, oriente o usuário a entrar na lista de espera pelo WhatsApp da secretaria.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Slug do curso. Ex: 'narracao-esportiva'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully burdens behavioral disclosure. It reveals that real enrollments are merged with a marketing curve, it never overstates spots, and handles soldOut. This is sufficient for safe usage.
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, front-loaded with purpose. No redundant information, but the soldOut guidance could be integrated more succinctly. Overall efficient.
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 a single required parameter, no output schema, and no nested objects, the description covers the tool's behavior and edge case (soldOut). It leaves no critical gaps 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?
Schema coverage is 100% for the single param, which already includes description and example. The description adds context about the param's purpose (course slug) but doesn't significantly extend beyond schema.
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 remaining spots in the next class of a course, using specific verb and resource. It distinguishes itself from sibling tools like get_course or list_courses by focusing on availability.
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 explains when to use the tool (check next class availability) and provides guidance for the soldOut condition (enter waitlist via WhatsApp). However, it doesn't explicitly state when not to use it vs alternatives.
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 has a clearly distinct purpose: course availability vs upcoming classes, detailed vs summary listings, separate tools for blog, podcast, services, teachers, radio, school info, and cross-content search. No overlapping tool descriptions.
Most tools follow a verb_noun pattern (list_, get_, check_), but 'radio_now_playing' and 'school_info' deviate slightly, and 'search' is a single verb. Nonetheless, the naming is mostly consistent and predictable.
15 tools is well-scoped for a school information server covering courses, blog, podcast, radio, services, teachers, and institutional info. Each tool serves a necessary function without redundancy.
The tool surface provides comprehensive coverage for informational queries about the school: all major content types (courses, blog, podcast, radio) have list/detail tools, real-time availability, search, and institutional info. No obvious gaps for a read-only knowledge server.