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set_grading_guidelines

Save course grading guidelines and a manual late-penalty scale locally to define how AI grades submissions and handles late work.

Instructions

Save grading guidelines and a manual late-penalty scale for a course (stored only on this computer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_idYes
guidelinesNoHow this grader wants work graded (leniency, extra credit, what counts as evidence...). Shown with every assignment.
late_penalty_tiersNoManual late scale, e.g. [{under_days: 1, percent: 0}, {under_days: 7, percent: 5, min_points: 1}, {under_days: null, percent: 10, min_points: 3}]

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses that data is 'stored only on this computer' (local-only persistence), which is real behavioral context, but it omits whether saving replaces vs. merges existing guidelines, what happens to unfilled fields, and 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?

A single front-loaded sentence with no filler; the storage caveat is parenthetically tucked in after the core action. Every clause earns its place.

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?

There is no output schema and no annotations, so the description should do more work. It covers purpose and storage locality but leaves key mutation semantics (overwrite behavior, partial-update behavior, failure modes) unaddressed despite being a save/create-style tool.

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 67%, and the schema itself richly documents guidelines and late_penalty_tiers (including the LateTier shape with an example-scale hint). The description adds only the framing 'manual late-penalty scale' and does not mention the required course_id, so it is roughly at baseline.

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?

States a specific verb ('Save') and resource ('grading guidelines and a manual late-penalty scale') scoped 'for a course', which clearly distinguishes it from the read-oriented sibling get_grading_guidelines. It does not name any sibling explicitly, but the write-vs-read split is inferable.

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?

Usage is implied by 'Save ... for a course', but there is no explicit when-to-use guidance, no mention of the get_grading_guidelines counterpart, and no stated preconditions or exclusions. The agent must infer the workflow context.

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