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prepare_for_interview

Read-only

Assembles everything needed to prepare for an interview on a saved GigHQ job application: the job and company context, the candidate's full profile, their Career Journal grouped by role (for STAR/CAR story material), which evidence best matches the role (to emphasize) and which competencies are weakly covered (to address or reframe), and a company briefing (Community Signal, H1B status, size). This read-only tool supplies context only — it does not generate interview questions, write STAR/CAR answers, or assess the candidate.

Input Schema

NameRequiredDescriptionDefault
application_uuidYesUUID of the saved GigHQ job application to prepare for.

Output Schema

NameRequiredDescriptionDefault
purposeYes
candidateNo
fit_signalNo
provenanceNo
applicationNo
career_journalNo
company_contextNo
missing_contextYes
fallback_guidanceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The annotations already establish read-only, closed-world, non-destructive behavior. The description adds useful behavioral context by disclosing the assembled categories and framing the tool as context-only. It does not mention permissions, rate limits, or response format, but that is less critical given the existing output schema and annotations.

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

Conciseness4/5

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

The description is front-loaded with the core action and then enumerates the assembled contents. It is dense but purposeful, with the second sentence clearly separating scope from non-goals. It is slightly long, but the detail is relevant to a multi-source aggregator.

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?

The tool aggregates data from several sources, and the description fully identifies what is assembled and what the tool does not do. An output schema exists, so return structure need not be explained, and the annotations already cover safety characteristics. Nothing essential for correct selection is missing.

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

Parameters3/5

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

There is one required application_uuid parameter, and the schema description already explains that it identifies the saved GigHQ job application. The description reinforces that this is tied to a saved application but does not add syntax or format details beyond the schema. With 100% schema description coverage, the baseline of 3 is appropriate.

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?

States a specific verb and resource: it assembles interview-preparation context for a saved GigHQ job application. The description enumerates exactly what is included (job/company context, profile, Career Journal by role, evidence matches, weak competencies, company briefing), making its purpose distinct from sibling getters.

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?

Clearly says when to use it: preparing for an interview on a saved GigHQ job application. It also states when not to expect more from it, explicitly noting that it does not generate questions, write STAR/CAR answers, or assess the candidate. It does not name specific alternative sibling tools, so it falls short of explicit alternatives.

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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