Prepare for a specific job interview by generating likely questions, STAR stories from profile facts, consistency checks against sent CV and cover letter, questions to ask, and gaps to address.
Interview prep for one application: likely questions, STAR stories from profile facts only, consistency reminders against the CV and letter that were sent, questions to ask, gaps to prepare.
| Name | Required | Description | Default |
|---|---|---|---|
| stage | No | first round | |
| job_id | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does add real behavioral context — STAR stories are grounded in 'profile facts only' (no fabrication) and reminders are checked against the CV and letter that were sent — but it never states whether the tool is read-only, what it returns or how long the output is, or what happens if the profile is sparse.
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?
A single front-loaded sentence with no filler; the colon-list keeps it dense. It reads as a run-on fragment list rather than clean prose, but every clause maps to a distinct deliverable.
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 two-parameter generative tool with no output schema and no annotations, the description usefully substitutes an output outline via its enumerated artifacts. It still leaves the undocumented 'stage' parameter and the return format unexplained, so it is adequate but not complete.
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 coverage is 0%, so the description must compensate. It only indirectly implies job_id identifies the application, and it says nothing at all about 'stage' (default 'first round', no enum), leaving the agent to guess what values are valid and how they change the output.
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?
Names a specific resource and scope ('Interview prep for one application') and enumerates the exact artifacts produced: likely questions, STAR stories, consistency reminders, questions to ask, gaps. No sibling tool covers interview preparation, so an agent can route here unambiguously.
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?
Usage is implied by the phrase 'for one application' and the enumerated outputs, but there is no explicit when-to-use/when-not guidance, no stated prerequisites (e.g. that a CV and letter must already exist for the consistency checks), and no pointer to related siblings like job_get or cv_outline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.