Skip to main content
Glama
dpkdhingra91

AI Interview Agents MCP Server

schedule_screened_candidates

Destructive

Schedule AI interviews for screened candidates by sending invite emails. Only includes candidates with valid emails; returns success or failure status per candidate.

Instructions

Schedule AI interviews for chosen candidates of a screening. Sends a real invite EMAIL to each and consumes interview quota.

    CONFIRM BEFORE CALLING:
    - Read back to the user which candidates (by name) you're about to invite
      and get explicit go-ahead. This spends money and emails real people —
      inviting the wrong candidates is not undoable in-band.
    - Pull candidate_ids from get_screening_results 'id' fields. Only include
      rows with hasEmail=true; rows with a placeholder email are REJECTED
      (the whole call 400s) until a real email is added.

    The interview reuses the screening role's stored config (JD, questions,
    evaluation focus) — same as the web 'Schedule these N' action. The
    candidate self-serves the interview when they open the link; you cannot
    pin a clock time. duration is seconds per interview (default 900 = 15 min).

    Returns {scheduledCount, scheduled_candidates:[{candidateId,
    candidate_email, status ('scheduled'|'needs_email'|'skipped_existing'|
    'failed'), meetingId, meetingUrl, reason}]}. Some rows may succeed while
    others fail or are skipped (already scheduled) — never assume the whole
    batch went out.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
role_idYes
durationNo
candidate_idsYes
Behavior5/5

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

Annotations destructiveHint=true warn of side effects, but description adds crucial context: sends real emails, consumes quota, cannot undo, returns partial successes. Explains duration default (900 sec), self-served interview (no clock time), and response format. No contradiction with 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?

Well-structured with a clear warning section and logical flow. Somewhat long but each sentence adds value. No redundancy. Minor deduction for length could be tightened.

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?

Given no output schema, description explains return shape, partial success/failure, error conditions (400 for missing email), and preconditions. Covers side effects, quota consumption, and usage pattern. Highly complete for a destructive batch tool.

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?

Schema has no descriptions (0% coverage). Description adds context: candidate_ids must come from get_screening_results with hasEmail=true, duration default 900. However, role_id is not explained, and other parameter nuances are missing. Partially compensates but not exhaustive.

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 schedules AI interviews for screened candidates, sending real emails and consuming quota. It distinguishes from siblings like schedule_interview (individual) and add_screening_candidates by specifying the screening context and batch scheduling.

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 'CONFIRM BEFORE CALLING' with steps: read back candidates, get go-ahead, pull IDs from get_screening_results with hasEmail=true. Warns about placeholder emails causing 400 and mentions the tool uses stored config like the web action. This gives clear when-to-use and when-not-to-use guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dpkdhingra91/aiia-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server