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This workflow walks through setting up resume and CV extraction through an MCP-connected AI agent. Affinda provides a purpose-built one-call setup that provisions everything needed for a recruitment pipeline. Trigger phrases that start this workflow:
  • “Set up a resume extractor”
  • “I want to parse CVs”
  • “Build a recruitment workspace”
  • “Read job descriptions automatically”
Before starting, your AI client must be connected to the Affinda MCP server. See MCP Connector and Plugin for setup instructions.

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Steps

1

Locate your organisation

The agent calls list_organizations to find your Affinda organisation. If you have more than one, it will ask which one to use.
2

One-call workspace setup

The agent calls create_recruit_workspace, which creates a “Recruitment” workspace pre-loaded with four document types in a single step:
Document typeWhat it extracts
ResumeFull structured candidate data: work history, education, skills, contact details, languages, certifications
Job DescriptionRole title, requirements, location, salary signals, responsibilities
Redacted ResumeSame fields as Resume with personally identifiable information removed
Resume SummaryAuto-generated condensed view of the candidate profile
The workspace is pre-configured with OCR mode, classification, and model memory settings tuned for the recruitment domain. No further configuration is needed for the standard pipeline.
create_recruit_workspace is idempotent at the workspace level: calling it again when a “Recruitment” workspace already exists in the organisation updates the existing one rather than creating a duplicate.
3

Upload resumes

Your workspace is immediately ready to receive files. Classification automatically routes uploads to the correct document type — you do not need to specify whether a file is a resume or a job description.Upload via the Affinda app (drag and drop), the API, or ask the agent: “Upload this resume: [URL or file path].”

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Extending after setup

Once the standard workspace is running, common follow-up requests include:
Ask your agent: “Add a candidate ID field to the Resume document type.”The agent will create a candidateId text field on the Resume document type. You can populate this field via the API when pushing candidates into your ATS.
Connect the “skills” field on the Resume document type to a data source of allowed skill values. This lets the system flag extractions that contain skills outside your taxonomy.Ask: “Validate resume skills against our approved skills list.” The agent will walk through the connect-validation-data workflow.
Configure an integration endpoint so confirmed documents automatically push structured data to your ATS or HRIS.Ask: “Set up an integration to push confirmed resumes to [your system].”
If you need a reduced or domain-specific field set (for example, only candidate name, email, and years of experience for a pre-screening tool), use the custom path instead of create_recruit_workspace:
  1. Ask the agent to create a workspace with create_workspace.
  2. Ask it to create a document type with create_document_type.
  3. Describe the fields you need and it will bulk-create them with bulk_create_fields.
This gives you full control over the schema, though you lose the pre-configured OCR and classification settings that create_recruit_workspace applies.

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What to expect after setup

  • Classification routes uploads automatically — no need to specify document type per upload.
  • Resume extraction covers a broad structured schema out of the box; no field configuration is required for the standard pipeline.
  • Model memory is enabled by default. Confirmed documents improve extraction accuracy over time.
  • If you need to search and rank candidates against job descriptions, see Affinda’s Search and Match product.