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vinayvobbili

jobwatch

by vinayvobbili
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_interview_prep

Read-onlyIdempotent

Build a recruiter-call or interview prep sheet that maps each posting requirement to your resume, flags gaps, and lists questions to expect and ask.

Instructions

A prep sheet for a recruiter call or interview: stage and next step, each requirement and responsibility in the posting next to the closest resume line (quoted, never written), gaps to be honest about, what was sent, questions to expect and to ask, and the posting. For an application added by hand, the posting is found on a watched board by its requisition id. markdown is the sheet ready to read. Also: brief, the company as the posting and the person's own notes tell it (about: the posting's lines; facts; notes; contacts: people they know there; source), nothing looked up elsewhere, so say it's from the posting; pay, the posted range (posted, posted_min, posted_max), the person's target (target, and target_max when it's a range) and how it sits against the range (target_note); and role_questions, questions for this role written from the posting's asks (questions_note says where they came from; empty: expect's generic questions stand). The person's private pay floor is never returned here (pay.floor_withheld says one is set): don't ask for it or guess it. With draft_answers, drafted holds the drafted questions and draft_note how they were made, or why there are none. practised holds the answers the person kept from mock interviews (jobwatch ask --job KEY --mock): question, answer as they said it, and improved, the tightened version built from their own words and stories. Present a drafted answer as the person's only where it cites their stories. story_gaps lists what a story would help with (ask, why): asks with nothing close on the resume, drafted questions with no story yet, missing must-haves; to fill one, interview the person and keep what they confirm with save_story. Use it before a call; get_job and get_application_package give the raw pieces.

Input Schema

NameRequiredDescriptionDefault
keyYesThe job: its key as other tools return it (source:board:posting id, e.g. greenhouse:acme:4012345), just the posting id, or the company's name when that names one job (the one queued or applied to there). When it matches several, the error lists their keys.
draft_answersNoAlso have jobwatch's own model (the chat's backend) draft 6-10 questions likely for this role, each with the ask it probes and an answer outline built only from the person's story file and resume, citing the story ids used; one with no story says so (no_story) with an honest angle. Invented story ids and numbers are taken out before they're returned. Slow the first time, then kept. With the claude backend the posting, resume and stories go to Anthropic. False is rule-based and instant.
role_questionsNoAlso have jobwatch's own model (the chat's backend) write questions for this role from the posting's asks (role_questions), without answers. Only the posting goes to the model, and to Anthropic with the claude backend. Slow the first time, then kept; questions saved earlier are always returned.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.5.0
    • addedInput schema / properties / draft_answers
      Added value: +{
+  "default": false,
+  "description": "Also have jobwatch's own model (the chat's backend) draft 6-10 questions likely for this role, each with the ask it probes and an answer outline built only from the person's story file and resume, citing the story ids used; one with no story says so (no_story) with an honest angle. Invented story ids and numbers are taken out before they're returned. Slow the first time, then kept. With the claude backend the posting, resume and stories go to Anthropic. False is rule-based and instant.",
+  "title": "Draft Answers",
+  "type": "boolean"
+}
    • addedInput schema / properties / role_questions
      Added value: +{
+  "default": false,
+  "description": "Also have jobwatch's own model (the chat's backend) write questions for this role from the posting's asks (role_questions), without answers. Only the posting goes to the model, and to Anthropic with the claude backend. Slow the first time, then kept; questions saved earlier are always returned.",
+  "title": "Role Questions",
+  "type": "boolean"
+}
  2. Addedv0.3.0

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description layers on substantial extra behavior: the private pay floor is deliberately never returned (pay.floor_withheld), resume lines are quoted and never written, invented story ids/numbers are stripped before return, and the model-backed modes send posting/resume/stories to Anthropic under the claude backend and are cached after a slow first call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded in the first sentence, but the body collapses into megasentences and fragment lists joined by colons and semicolons, making it taxing to parse. Nearly every clause carries information, so there is little true filler, but the structure is poor for an agent skimming for a decision.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description must carry the return shape itself, and it does: it covers the sheet contents, brief, pay (posted/target/target_note), role_questions, drafted answers, practised answers, and story_gaps with the follow-up tool (save_story). A complex multi-mode tool is described completely enough to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description goes beyond the schema by describing what each optional flag produces: draft_answers yields drafted/draft_note or an explanation for none, practised returns kept mock answers with an improved version, and role_questions falls back to generic expect questions when empty. It clarifies output semantics of the flags rather than just restating them.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening clause states a specific verb+resource (a prep sheet for a recruiter call or interview) and then enumerates exactly what the sheet contains. It also explicitly distinguishes itself from siblings by noting that get_job and get_application_package give the raw pieces this tool assembles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Use it before a call; get_job and get_application_package give the raw pieces" gives a clear use moment and names the alternatives for a different need. It falls short of 5 because it never states when this tool is unnecessary or the prerequisite state of the job (e.g. applied vs queued), leaving some routing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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