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Get the Learnosity MCP Feedback Link

get_feedback_link
Read-only

Call this when a user wants to give feedback, report a problem, or suggest an improvement about the Learnosity MCP server or its documentation. Returns a friendly message and a link to the Learnosity MCP Feedback Page to share with the user. This does not collect feedback in the chat. Do not ask the user for their email, credentials, ratings, or any structured feedback fields in the chat - the linked page collects everything.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, destructiveHint false, openWorldHint), but the description adds genuinely useful behavior: the tool returns a message and link rather than recording anything, and the agent must not solicit data in chat. That is real context beyond the structured fields, though it says nothing about output shape (covered by the output schema).

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?

Front-loaded with the triggering condition and tight throughout, with no filler. There is mild repetition between 'This does not collect feedback in the chat' and 'Do not ask the user for their email... in the chat', which could be merged into one sentence.

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 zero-parameter, read-only tool with an output schema, the description covers everything an agent needs: when to invoke it, what it produces, and the constraint against gathering data in conversation. Nothing material is missing.

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

Parameters4/5

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

The tool takes no parameters, so there is no parameter meaning to add; the baseline for a zero-parameter tool applies. The description correctly implies there is nothing to supply beyond the call itself.

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?

The description states a specific trigger (user wants to give feedback, report a problem, or suggest an improvement) and a concrete outcome (a friendly message plus a link to the Learnosity MCP Feedback Page). It also delimits scope by saying it applies to the Learnosity MCP server or its documentation, which cleanly separates it from the docs- and skills-related siblings.

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

Usage Guidelines5/5

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

It gives an explicit when-to-use condition and explicit exclusions: do not collect feedback in the chat, and do not ask for email, credentials, ratings, or structured fields. An agent knows both when to call it and what not to do afterwards.

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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