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edubase_post_exam_summary

Submit a brief AI-generated exam summary to EduBase to enable automated post-exam evaluation and reporting.

Instructions

Submit a short summary of an exam, such as an AI-generated evaluation of its results. Keep it concise, use only basic HTML formatting, and avoid personal information (names, usernames, contact details).

Input Schema

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changedv1.1.2
    • addedInput schema / $schema
      Added value: +"http://json-schema.org/draft-07/schema#"
    • changedInput schema / properties / llm / description
      Previous value: -"Name of the Large Language Model used to generate the summary.\n- preferred values: openai / claude / gemini"New value: +"name of the Large Language Model used to generate the summary (preferred: openai / claude / gemini)"
    • changedInput schema / properties / model / description
      Previous value: -"Exact LLM model name used to generate the summary"New value: +"exact LLM model name used to generate the summary (requires llm)"
    • changedInput schema / properties / summary / description
      Previous value: -"Summary text. \n- basic HTML formatting allowed, but avoid complex designs\n- keep the summary short and concise\n- try to avoid including personal information (such as usernames, names and contact addresses)"New value: +"summary text (basic HTML formatting allowed, keep concise, avoid personal information)"
    • changedInput schema / properties / type / description
      Previous value: -"Type of summary. (default: ai)\n- ai: AI-generated summary"New value: +"type of summary (default: ai)"
    • addedInput schema / properties / type / enum
      Added value: +[
      +  "ai"
      +]
    • changedInput schema / required
      Previous value: -[
      -  "exam",
      -  "type",
      -  "summary",
      -  "llm",
      -  "model"
      -]New value: +[
      +  "exam",
      +  "summary"
      +]
  2. First observedv1.0.22

TDQS

A3.5/5.0
Behavior3/5

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

Annotations carry the safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false), so the bar for additional disclosure is lower. The description adds useful context by requiring concise content, basic HTML only, and no personal information — a meaningful privacy constraint. It does not, however, disclose whether an existing summary is overwritten, what happens on duplicate submission, or any auth requirements.

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

Conciseness5/5

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

Two sentences, roughly 30 words, with the action front-loaded ('Submit a short summary of an exam') followed by three quick constraints. Every clause earns its place, and there is no fluff or repetition within the description itself.

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

Completeness3/5

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

For a simple content-submission tool with 2 required parameters, full schema coverage, and an explicit content policy, the description is adequate. However, with no output schema and no mention of overwrite behavior, conflict handling, or where this fits in the exam lifecycle, an agent cannot fully anticipate the tool's effects beyond the write operation.

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 description coverage is 100%, so the schema already documents all 6 parameters, setting the baseline at 3. The description largely restates the summary parameter's own schema text ('keep concise, avoid personal information, basic HTML formatting'), adding no new meaning for exam, llm, model, type, or language parameters. It neither compensates for a gap nor enriches the schema's already complete documentation.

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 uses a specific verb+resource pairing: 'Submit a short summary of an exam,' with a clarifying example ('such as an AI-generated evaluation of its results'). This distinguishes it from sibling tools like edubase_post_exam (creating an exam) and edubase_post_exam_results_export (exporting results). It does not name a sibling explicitly, so it falls short of a 5, but the purpose is unambiguous.

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

Usage Guidelines3/5

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

The description gives usage context by framing the summary as an AI-generated evaluation of exam results, and it sets content constraints ('Keep it concise, use only basic HTML formatting, and avoid personal information'). However, it never states when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites (e.g., whether an exam must already exist).

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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