Generate a post-application learning plan that reviews outcomes and weak topics to improve future job-hunting campaign results.
Generate a post-mortem learning plan for an application
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Application slug | |
| notes | No | Additional notes | |
| steer | No | Custom LLM instructions | |
| status | No | Status at the time of writing | |
| campaign | Yes | Campaign name (e.g. "default") | |
| weakTopics | No | Weak topics to include |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It says it generates a plan but does not state whether this is a read or write operation, whether it creates or modifies records, what permissions are required, or what side effects occur. The single verb 'Generate' gives minimal behavioral context.
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, front-loaded sentence with no wasted words. It immediately states the action and output, making it easy to scan and parse.
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?
Given six parameters, no output schema, and no annotations, the description is too thin. It does not explain what a 'post-mortem learning plan' contains, how parameters like steer, weakTopics, or status affect the output, or when this tool should be chosen over related retros tools. The schema covers parameter descriptions, but the definition lacks sufficient context for correct 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 all six parameters are already documented in the schema. The description adds no meaning beyond the schema, only saying the plan is 'for an application', which loosely relates to the slug parameter. Baseline 3 is appropriate when the schema does the heavy lifting.
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 and resource: 'Generate a post-mortem learning plan for an application'. It clearly tells the agent what the tool produces. However, it does not distinguish the tool from similar siblings such as aggregate_retros, append_retro, or prepare, leaving the agent to infer the boundary.
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 offers no guidance on when to use this tool versus alternatives. It does not mention prerequisites, timing, or exclusions, and it does not reference any sibling tool. The agent is given only the bare purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.