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edubase_post_exam_summary

Submit an AI-generated summary for an exam to EduBase. Provide exam ID, summary text, and optionally specify LLM and model used.

Instructions

Submit a new AI exam summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
examYesexam identification string
languageNosummary language
typeNotype of summary (default: ai)
summaryYessummary text (basic HTML formatting allowed, keep concise, avoid personal information)
llmNoname of the Large Language Model used to generate the summary (preferred: openai / claude / gemini)
modelNoexact LLM model name used to generate the summary (requires llm)
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate non-read-only and non-destructive, but the description adds minimal behavioral context. It does not disclose side effects, permission requirements, rate limits, or the nature of the creation process (e.g., whether multiple summaries can exist per exam).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that directly conveys the purpose. It is concise but could optionally include more context without being verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema and the tool's action of creating a resource, the description lacks information about return values, confirmation, or constraints. It does not cover what happens after a successful submission or error scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so parameters are already documented. The description does not add extra meaning beyond what the schema provides. The baseline is 3, which is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Submit a new AI exam summary' clearly states the verb (Submit), resource (exam summary), and distinguishes it from other exam-related 'post' tools like edubase_post_exam or edubase_post_exam_branding. However, it could be more specific about what constitutes an 'AI exam summary'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites, required conditions, or when not to use it, leaving the agent to infer usage entirely from the tool name and context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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