Persist the learning goal and its constraints for a session, enabling state-driven tutoring and progress tracking.
Persist the learning goal and its constraints (lifecycle Stage 1).
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | ||
| materials | No | ||
| request_id | No | ||
| session_id | Yes | ||
| constraints | No | ||
| time_budget | No | ||
| target_depth | No | ||
| target_domain | No | ||
| target_outcome | No | ||
| prior_knowledge | No | ||
| assessment_requirements | No |
Changes observed during successful MCP inspections.
v2.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only says 'Persist.' It does not mention whether an existing goal is overwritten, whether the session must already exist, what happens when nullable fields are omitted, or whether there are side effects. The description is not misleading, just under-specified.
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 a single short sentenceholistic and front-loaded, which is efficient IA-wise. However, it is concise at the expense of useful context, and the unexplained 'lifecycle Stage 1' phrasing adds jargon rather than clarity for a tool with 11 parameters.
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 an 11-parameter persistence tool with no annotations, no output schema, and no parameter descriptions, this definition is incomplete. The agent cannot determine what Stage 1 means, what success/failure looks like, or how this tool relates to get_learning_goal and set_learning_configuration.
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?
Schema description coverage is 0%, so the description needed to compensate, but it only references 'goal' and 'constraints' generically. It does not clarify time_budget, target_depth, request_id, assessment_requirements, or how the many nullable fields interact. The parameter names are self-explanatory to some degree, but the description adds little semantic value.
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 uses a specific verb ('Persist') and a concrete resource ('the learning goal and its constraints'), and the lifecycle reference helps set it apart from the read-only sibling get_learning_goal. It is clear but stops short of explaining what 'persist' means operationally (create vs. update).
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?
There is no guidance on when to use this tool versus save_knowledge_nodes, set_learning_configuration, or complete_session. The 'lifecycle Stage 1' hint is unexplained, so the agent must infer context that should have been explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.