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chrischall

artsonia-mcp

by chrischall
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TypeScript
Remote

Mark a student's feedback as read

artsonia_mark_feedback_read

Mark a student's teacher feedback as read after confirmation, clearing all feedback at once since per-item updates are unavailable.

Instructions

Mark the student's teacher feedback as read (this is a mark-ALL action — Artsonia has no per-item control). Asks the user to confirm first: a confirmation prompt where the client supports one; otherwise the first call returns a preview and a confirmToken, and only a repeat call with that token proceeds (see MCP_CONFIRM_MODE).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
artist_idYesStudent artist_id (from artsonia_list_students).
confirmTokenNoONLY for the two-step confirmation fallback (a client without MCP elicitation). The confirmToken from this same tool's phase-1 "confirmation-required" response, passed back ONLY after the user has seen that preview and explicitly approved it in chat — never on the first call, never invented, never reused. Call again with the same arguments. Ignored when the client supports elicitation.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.1

TDQS

A4.4/5.0
Behavior5/5

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

Annotations establish it is not read-only and not destructive, and the description adds crucial context: the action is global (mark-ALL, no per-item control) and requires a confirmation step. It details both the elicitation-based prompt and the confirmToken fallback, going well beyond the annotations.

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 front-loaded with the core action and then explains the confirmation behavior efficiently. It is dense but every sentence contributes, though it could be slightly more scannable with fewer parentheticals.

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

Completeness5/5

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

For a non-destructive mutation with a confirmation workflow and no output schema, the description covers the required behavior, the mark-all limitation, and both confirmation paths. Nothing essential to correct invocation is missing.

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%, and both parameters are fully documented in the input schema, including the confirmToken's intricate constraints. The description paraphrases the confirmToken flow but adds no semantic detail beyond what the schema already provides.

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

Purpose5/5

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

States a specific verb and resource: mark the student's teacher feedback as read. It also explicitly notes the mark-ALL scope, distinguishing it from any per-item action and from the sibling artsonia_get_feedback.

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

Usage Guidelines4/5

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

Clearly explains the context in which the tool is invoked and that it requires user confirmation first, including the two-step fallback flow. It does not name explicit alternatives or when-not-to-use cases, but none are obviously needed for a mark-read action.

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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