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AI Interview Agents MCP Server

create_screening_role

Set up a screening role to rank CVs against a job description. Specify must-have skills, experience, and requirements to score candidates objectively.

Instructions

Create a Screening — a role to rank CVs against a JD before spending any interview credit. Returns {"roleId": , ...}; keep that roleId for the rest of the flow.

    THE SCREENING FLOW (call these tools in order):
    1. create_screening_role  ← you are here
    2. parse_cvs(files=...)    — extract candidates from resume files
    3. add_screening_candidates(role_id, candidates=<parse_cvs rows>)
    4. run_screening(role_id)  — starts async CV-vs-JD scoring
    5. get_screening_results(role_id) — poll until scored, read the ranking
    6. schedule_screened_candidates(role_id, candidate_ids) — invite the good ones

    FIELDS:
    - name: the screening / role title (required, e.g. "Senior Backend Engineer").
    - job_description: paste the full JD text if you have it — it drives the
      score most. Optional but strongly recommended.
    - must_have_skills vs nice_to_have_skills: skills the role weighs heavily
      vs would-be-nice. Neither AUTO-REJECTS anyone — they shape the LLM
      score and the matched/related/missing breakdown.
    - experience: free text, e.g. "5+ years".
    - location + work_mode: work_mode is 'remote', 'onsite', or 'hybrid'.
      Location is a soft signal only (a remote role ignores it); nobody is
      dropped for being in the wrong city.
    - additional_requirements: free text the model folds into scoring, e.g.
      "valid work visa", "bachelor's degree", "AWS certified".
    - language: 'en', 'hi', or 'ar'. company_name: optional.

    This creates a role record (screening_enabled). If the user already has a
    screening for this opening, call list_screening_roles first and reuse its
    roleId instead of making a duplicate.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
languageNoen
locationNo
work_modeNo
experienceNo
company_nameNo
job_descriptionNo
must_have_skillsNo
nice_to_have_skillsNo
additional_requirementsNo
Behavior4/5

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

Annotations declare non-read-only, non-destructive, non-open-world. Description adds that it creates a role record, returns roleId, does not auto-reject, and that location is a soft signal for remote roles. No contradictions.

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?

Well-structured with numbered flow, fields list, and usage notes. Front-loaded with return value. Slightly long but justified by complexity of 10 parameters and flow context.

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?

Provides return format (roleId), full flow, parameter details, and duplicate avoidance guidance. Lacks output schema but description compensates adequately for the tool's complexity.

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

Parameters5/5

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

Schema coverage is 0% but description explains each of the 10 parameters with examples, constraints (e.g., required name, soft signals, language enum), and behavioral impact (e.g., job_description drives scoring).

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 the tool creates a screening role, part of a CV ranking flow, and distinguishes it from sibling tools by explaining the overall flow and when to use list_screening_roles instead.

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

Usage Guidelines5/5

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

Explicitly provides when to use (creating a new screening), when not to (reuse existing roleId from list_screening_roles if duplicate), and outlines the complete six-step flow with tool names.

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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