get_my_learning_overviewRead the connected learner's Drivings profile, study target and recent learning activity. Requires Drivings sign-in and explicit OAuth consent. It never changes learner or account data.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes |
Changes observed during successful MCP inspections.
Output schema / properties / data / properties / generatedAtAdded value: +{
+ "type": "string"
+}Output schema / properties / data / properties / schemaVersionAdded value: +{
+ "const": "2026-07-22",
+ "type": "string"
+}Output schema / properties / data / requiredPrevious value: -[
- "learner",
- "study",
- "progress",
- "privacy"
-]New value: +[
+ "schemaVersion",
+ "generatedAt",
+ "learner",
+ "study",
+ "progress",
+ "privacy"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, lowering the burden. The description adds valuable auth context ('Requires Drivings sign-in and explicit OAuth consent') and reinforces non-mutation with 'It never changes learner or account data.' This goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the core purpose, the second covers auth and side-effect clarity. Every sentence earns its place; no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 is fully complete: it states what is read, the auth requirement, and the non-mutating nature. There is no missing context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so with schema coverage at 100% the baseline is 4. The description does not need to explain parameters, and it actually compensates by describing what data the tool returns, which is sufficient for a no-input tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Read') and a specific resource ('the connected learner's Drivings profile, study target and recent learning activity'). This clearly differentiates it from sibling tools like get_driving_guide or find_test_centres, which are about general content rather than the learner's personal overview.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when you need the learner's profile, target, or activity) but does not explicitly contrast it with siblings or state 'use this instead of X'. There is no mention of alternatives or exclusions, so the guidance is clear but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.