Candidate evaluation tools for AI 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. MCP (Model Context Protocol) is the open standard that lets AI agents discover and call external tools — in this case, candidate evaluation.
Currently onboarding a small group of platform partners for early MCP access. Public registry listing and self-serve setup are not live yet — request sandbox access and every onboarding starts with a scoping call.
# MCP client → tools/list{ "tools": [ { "name": "create_evaluation", "description": "Trigger an AI technical interview" }, { "name": "check_evaluation_status", "description": "See where a candidate is in the flow" }, { "name": "get_scorecard_summary", "description": "Retrieve the human-reviewed scorecard" } ]}Illustrative tool listing — names simplified; the exact server definition is shared during partner onboarding.
Tool surface
Tools your agent gets
Three explicit, scoped actions instead of open-ended access: an agent can start an evaluation, follow its progress, and pull back the human-reviewed result. Each tool maps to the same evaluation workflow behind the REST API — nothing here is a separate product.
create_evaluation
Request an AI technical interview for a candidate at the technical-screen stage. Recruo handles the invite, the adaptive interview, and the secure-browser session.
{ "candidate_ref": "string", "role": "string", "focus_areas": "string[]" }check_evaluation_status
Let the agent follow where a candidate is — invited, in progress, under human review, or completed — without a recruiter chasing updates by hand.
{ "evaluation_id": "string" }get_scorecard_summary
Retrieve the human-reviewed scorecard the agent can summarize for recruiters: scores, AI-skills evidence, secure-browser signals, red flags, and the verdict.
{ "evaluation_id": "string" }Agent call
What an agent call looks like
An MCP JSON-RPC exchange, illustrative of the real workflow: the agent calls a scoped tool, Recruo returns the evaluation result, and the assistant surfaces the evidence to the hiring team — it never makes the hiring decision itself.
# Agent → Recruo MCP server (tools/call){ "jsonrpc": "2.0", "id": 7, "method": "tools/call", "params": { "name": "get_scorecard_summary", "arguments": { "evaluation_id": "eval_9f2c4a" } }} # ← Result the agent can surface to the hiring team{ "jsonrpc": "2.0", "id": 7, "result": { "content": [ { "type": "text", "text": "Oleksandr K. — 87/100, PROCEED. Hard skills 8.6/10 avg across RAG, evals and fine-tuning. English C1. Secure-browser session clean, no integrity flags. Human review: proceed to final interview." } ] }}Scorecard values mirror the real sample on the scorecard page: overall 87/100, hard-skills average 8.6/10, English C1, human-reviewed verdict.
Works with any MCP client
MCP is an open protocol, so compatibility comes from the standard rather than from partnerships. Any MCP-capable client — Claude, ChatGPT, or your own agent runtime — can discover and call tools on a server like this one.
Prefer conventional system-to-system integration instead of an agent? The REST integration path →
Agentic hiring workflow
How it fits an agentic hiring workflow
MCP is the technical companion to the API page: it makes evaluation actions discoverable and usable by AI agents under explicit boundaries, while final decisions stay with the hiring team.
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.
Scoped tools
Agents get explicit candidate-evaluation actions with least-privilege permissions — never open-ended access to candidate data.
Human approval
Assistants surface evidence; hiring decisions stay with humans, with recruiter review built into the evaluation itself.
Audit and consent
Candidate consent, audit logging, GDPR-aware handling, and EU AI Act aligned controls apply whether the caller is a person or an agent.
Use cases
Where the integration creates leverage
Written for teams that already have a hiring workflow and want Recruo's technical evaluation layer inside it — not another tool to log in to.
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.
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?
An agent gets explicit candidate-evaluation actions: 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?
Not yet. Access is partner-gated: there is no public MCP registry listing, public endpoint, or self-serve setup today. Request sandbox access and we scope the connection to your agent stack together during partner onboarding.
MCP sandbox access
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 sandbox access and partner MCP onboarding.