Candidateevaluation toolsforAI hiring agents.
Expose Recruo's technical interview and scorecard workflow to internal hiring assistants, ATS agents, and agent-native recruiting systems through an MCP-style integration surface.
Currently onboarding a small group of platform partners for early MCP access. Public registry listing and self-serve setup are not live yet — every onboarding starts with a scoping call.
Workflow
Agent to evaluation
MCP is the technical companion to the API page: it makes evaluation actions discoverable and usable by AI agents under explicit boundaries.
Agent receives hiring context
An internal assistant or ATS agent has a candidate, role, and hiring-stage context that requires technical validation.
Agent calls a scoped Recruo tool
The MCP-style surface exposes explicit actions such as create evaluation request, check status, and retrieve scorecard summary.
Recruo runs the evaluation workflow
The candidate completes the technical interview with scorecard output, secure-browser signals, AI-skills evidence, and human review.
Agent surfaces evidence to humans
The assistant returns scorecard highlights, risk signals, and audit evidence while keeping final decisions with the hiring team.
Hiring agents need trustworthy tools
An AI assistant can coordinate hiring work, but it still needs a reliable evaluation system instead of inventing technical judgment from loose notes.
MCP makes evaluation actions discoverable
A scoped MCP-style interface can expose candidate evaluation, status checks, scorecard summaries, and audit evidence to agentic hiring workflows.
Agents should surface evidence, not decide alone
Recruo scorecards give assistants structured signals they can summarize for recruiters and hiring managers while preserving human review.
Use cases
Where the integration creates leverage
The pages are written for teams that already have a hiring workflow and want Recruo's technical evaluation layer inside it.
Internal hiring assistants
Let an internal assistant request technical evaluation and return scorecard highlights during candidate review.
Agentic recruiting workflows
Give AI hiring agents a bounded tool surface for evaluation actions instead of unstructured interview-note prompts.
ATS automation teams
Connect candidate-stage events to evaluation requests and status summaries without changing the ATS as source of truth.
Recruiting operations
Use structured scorecards and status checks to reduce manual candidate follow-up and evaluation chasing.
Compliance-sensitive hiring
Keep agent outputs tied to explicit tools, audit evidence, and human-reviewed candidate evaluation results.
Talent intelligence products
Surface Recruo evaluation summaries inside candidate intelligence or hiring copilot experiences.
Product proof
Built from the evaluation system already used in Recruo hiring
The integration story is anchored in visible product proof: structured AI interviews, scorecards, secure-browser validation, human review, and compliance posture.
Compliance posture
Tool access should be scoped to explicit candidate-evaluation actions.
Agent outputs should surface evidence for human review instead of making final hiring decisions.
Auditability and candidate data controls remain part of the evaluation layer.
Availability language is limited to partner or private integration access.
AI interview API
Use the REST and webhook page if your buyer is looking for conventional system-to-system integration.
Sample scorecard
Review the structured candidate output that an agent could retrieve and summarize for hiring teams.
Security and compliance
Ground MCP access in candidate data protection, audit logs, human review, and EU AI Act alignment.
Agentic AI hiring expertise
Connect the MCP page to Recruo content for teams hiring engineers who build agentic systems.
AI interview agents
Read how interview agents compare with traditional screening and where structured evaluation matters.
GDPR and AI recruitment
Understand the privacy and compliance context around AI-assisted recruitment workflows.
FAQ
Questions integration buyers usually ask first
Short answers for search visitors and procurement-minded teams before they start an integration conversation.
How is a recruiting MCP server different from the REST API?
The REST API is the direct system-to-system integration surface. MCP is the agent-native surface, designed so an internal assistant or hiring agent can discover tools, request evaluations, check status, and retrieve scorecard summaries.
What tools would an AI hiring agent use?
The page describes tools such as creating an evaluation request, checking candidate evaluation status, retrieving scorecard summaries, and surfacing compliance or audit evidence for review.
Can this connect to our ATS agent or internal assistant?
That is the intended partner-access conversation: expose Recruo evaluation actions to agentic hiring workflows while your ATS or internal system remains the source of truth.
What security boundaries matter for MCP access?
A recruiting MCP workflow should be scoped to explicit tools, least-privilege permissions, candidate consent, audit logging, and human approval before hiring decisions are finalized.
Does MCP change the compliance posture?
No. MCP changes how tools are exposed to agents. The underlying evaluation still needs GDPR-aware handling, human review, audit evidence, and EU AI Act aligned controls.
Is the Recruo MCP server public today?
This page uses partner/private integration language. It does not claim a public MCP registry listing, public endpoint, or self-serve setup unless those are launched separately.
MCP integration inquiry
Explore Recruo tools for your hiring agents.
Tell us what assistant, ATS agent, or internal workflow you are building. We'll reply within 24 hours about partner MCP access.