Retrieve auto-generated insights on past mistakes to avoid repeating them before retrying failed approaches or resuming work.
Get actionable Learnings — auto-generated insights about mistakes to avoid. CALL THIS before retrying an approach that failed before, or when picking up work where past problems may have been recorded.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Look back N days (default 7) | |
| project | No | Filter by project name |
Changes observed during successful MCP inspections.
v0.20.7Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the burden. 'Get' implies a read-only retrieval and 'auto-generated insights' describes the content type, which is useful. Still, it does not disclose any behavioral details such as whether results are cached, how many results are returned, or whether any state changes occur. Acceptable but not rich.
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?
Two sentences, no filler. The core purpose is front-loaded and the usage directive is immediate. Every sentence earns its place.
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 simple retrieval tool with two optional parameters, the description plus complete schema coverage is largely sufficient. It tells the agent what to retrieve, when to call it, and how to filter. The lack of output schema or mention of result format is a minor gap, but not critical for invocation.
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 100%, so the parameters are already documented with defaults and meanings. The description adds framing around when to use the tool but does not add semantic detail about the days or project parameters beyond the schema. Baseline 3 is appropriate.
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 clear verb+resource: 'Get actionable Learnings — auto-generated insights about mistakes to avoid.' It communicates what the tool returns and why it exists, but it does not explicitly distinguish itself from sibling tools like hicortex_lessons, so it falls short of a 5.
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 gives explicit when-to-use guidance: call this before retrying a previously failed approach or when picking up work where past problems may be recorded. However, it does not say when not to use it or name alternative tools, so it stops short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.