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dpkdhingra91

AI Interview Agents MCP Server

parse_cvs

Extract candidate name, email, and phone from CV files (PDF/DOCX) with OCR fallback. Returns deduplicated candidates ready for interview scheduling.

Instructions

Extract candidate identity (name, email, phone) from CV/resume files (PDF or DOCX, up to 20 per call). Runs OCR fallback for image-based PDFs. Returns deduplicated candidate rows ready to pass to schedule_interview.

    Each entry in 'files' must have:
      - 'name': filename including extension
      - 'bytes_base64': base64-encoded file contents

    If role_id is provided, dedups against candidates already attached to
    that role. Files exceeding 20 are rejected — batch across multiple calls.

    After parsing, ALWAYS show the user the extracted name/email/phone for
    each file before scheduling, then call schedule_interview with the
    returned 'candidates' list.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesYes
role_idNo
Behavior5/5

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

Annotations indicate non-destructive read but description adds context: OCR fallback, dedup logic, rejection of over 20 files. 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 purpose first then details. Slightly wordy but each sentence adds value. Could be tightened slightly.

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?

No output schema, but description mentions returning candidate rows ready for schedule_interview. Could specify structure more precisely. Covers error handling and batch guidance.

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?

Despite 0% schema description coverage, the description fully explains the 'files' array requires name and bytes_base64 fields, and role_id for dedup. Schema is minimal but description compensates.

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?

Description clearly states the tool extracts candidate identity from CV/resume files, handles PDF/DOCX, and includes OCR fallback. It distinguishes from siblings like schedule_interview, add_screening_candidates, etc.

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 tells when to use: after parsing, show results before scheduling, then call schedule_interview. Also advises batching if more than 20 files.

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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