Report the outcome of applying one or more Younanix lessons in production. Used to build the symbiotic feedback loop. Rate-limited to 100 reports per agent per day. Requires x-agent-key.
| Name | Required | Description | Default |
|---|---|---|---|
| outcome | Yes | ||
| lesson_ids | Yes | UUIDs of the lessons applied | |
| skill_name | No | ||
| outcome_evidence | No | ||
| mttr_minutes_observed | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose a rate limit ('Rate-limited to 100 reports per agent per day') and an auth requirement ('Requires x-agent-key'), which are valuable. However, it does not mention side effects, idempotency, or error behavior.
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 three concise sentences, each adding distinct value: the action, the purpose, and operational constraints. No unnecessary words.
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?
The tool has 5 parameters and no output schema. The description covers the core purpose and key constraints but lacks return value details and fails to differentiate from similar record_* siblings. It is adequate but leaves gaps for a 5-parameter tool.
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 only 20% (only lesson_ids has a description). The description mentions 'lesson_ids' and 'outcome' implicitly but does not explain skill_name, outcome_evidence, or mttr_minutes_observed. It fails to compensate for the low schema coverage.
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 and resource: 'Report the outcome of applying one or more Younanix lessons in production.' This clearly identifies the tool's function and distinguishes it from sibling tools like record_reflection or submit_incident_report.
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 phrase 'Used to build the symbiotic feedback loop' implies a context for use, but the description does not explicitly state when to prefer this tool over alternatives (e.g., record_younanix_usage) or when not to use it. Usage is implied rather than 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.
Most task-run and record_* tools are clearly distinct, but query_lessons, query_lessons_full, and query_lessons_premium all describe nearly the same underlying lesson-query action separated mainly by access tier and payment. An agent can easily call the wrong one, especially because 'full' and 'premium' boundaries are unclear.
The tools consistently use snake_case verb_noun names such as start_task_run, complete_task_run, record_reflection, and submit_incident_report. The query_lessons variants use a predictable suffix pattern, and there is no mixing of naming conventions.
Ten tools is a reasonable footprint, but the three query_lessons variants are essentially reimplementations of the same query by payment/access tier. The count is not excessive, though it could be reduced without losing real capability.
The set covers task-run start/completion and lesson querying, but there is no way to read back task runs, reflections, subagent traces, or usage records after submitting them. This makes the task-run side feel mostly write-only and creates a notable gap in a memory-focused server.