get_blog_postDetalhe completo de um post do blog incluindo o corpo em markdown (convertido do PortableText do Sanity). Útil pra LLMs lerem o conteúdo na íntegra.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Slug do post. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description partly covers behavior (mentions markdown conversion) but omits safety traits (e.g., read-only nature), authentication needs, error handling, or response structure. Basic behavioral context is present but incomplete.
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?
The description is concise with two sentences, front-loading the tool's purpose and unique value. Every word contributes meaning without redundancy.
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 one-parameter tool with low complexity, the description covers core functionality, format, and use case. However, without an output schema, it could explicitly list returned fields (e.g., title, date) to be fully complete.
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 description coverage is 100% for the single parameter (slug), so the baseline is 3. The description does not add meaning beyond the schema, as it only restates the retrieval by slug.
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 specifies the tool retrieves complete blog post details including the body in markdown format, converted from PortableText. It explicitly states the resource (blog post) and the purpose (useful for LLMs to read full content), distinguishing it from sibling list tools.
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 implies the tool is for reading full content but does not explicitly state when to use it versus alternatives like list_blog_posts. No exclusions or contrast with sibling tools are provided, resulting in implied but not explicit guidance.
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.