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mock_interview

Simulate a job interview with an AI interviewer. Provide a company and interview type to get a tailored briefing and post-interview feedback.

Instructions

Start a mock interview session. Returns a briefing with all application materials, your profile, and interviewer guidance so Claude can play the interviewer role.

After the session, save feedback and areas to improve using save_application_file.

Args: company: Company name interview_type: One of: behavioral, technical, culture, manager

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyYes
workspace_dirNo
interview_typeNobehavioral

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyYes
profileYes
materialsYes
interview_typeYes
interviewer_guidanceYes
interview_descriptionYes
Behavior3/5

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

No annotations provided, so the description carries full burden. It implies the tool is read-only by saying it 'returns a briefing' and does not mention modifications, but does not explicitly state that no data is changed. It discloses the interactive nature (Claude plays interviewer) but lacks detail on side effects or state changes.

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

Conciseness5/5

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

The description is concise: two sentences plus an Args list. No unnecessary words, front-loaded with the core purpose, and structured for quick parsing.

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

Completeness4/5

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

Given there is an output schema (not shown), the description doesn't need to detail return values. It covers the tool's action, prerequisites (company), and post-use advice. Missing details on workspace_dir parameter and potential statefulness, but overall sufficient.

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?

The input schema has 3 parameters with no descriptions (0% schema_description_coverage). The tool description compensates by documenting 'company' and 'interview_type' with allowed values, but omits 'workspace_dir'. This adds meaning beyond the schema but is incomplete.

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 description clearly states it starts a mock interview session and returns a briefing with application materials. It also instructs on the subsequent action (using save_application_file). This distinguishes it from siblings, none of which focus on mock interviews.

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?

The description explains when to use the tool (to start a mock interview) and what to do after (save feedback). It does not explicitly state when not to use it or list alternatives, but given no similar siblings, this is adequate.

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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