WorkorAI

Overview

WorkorAI is a talent marketplace exposed to agents through an MCP server (streamable HTTP at https://workorai.com/mcp, listed on the official MCP Registry as io.github.work0r-ai/workorai). This skill routes requests by intent across the dual-role tool surface: 9 candidate.* tools (job search, job detail, applications, apply, invitations, saved jobs) and the employer.* tools (job lifecycle, candidate discovery, invitations, applicant review). Employer candidate discovery returns tiered rankings (best/good/weak) with a white-box match explanation per candidate — fit score, skills proven in interview, gaps, and a quotable rationale — instead of a black-box score.

When to Use This Skill

• Use when a user asks to find a job, search vacancies, apply to a position,

or track their applications ("find me a job", "ищу работу").

• Use when an employer wants to post, publish, update, close, or archive a

job on WorkorAI.

• Use when an employer asks to find, rank, compare, or evaluate candidates,

or asks why a candidate matches a role.

• Use when a user needs to set up or troubleshoot the WorkorAI MCP

connection and API key onboarding.

How It Works

Step 1: Connect the MCP server

Add the WorkorAI MCP server to your agent's MCP configuration. For Claude Code:

bash
claude mcp add --transport http workorai https://workorai.com/mcp

If the user has no API key yet, call the request_access tool and follow the onboarding it returns.

Step 2: Route by role and intent

Detect whether the request is a candidate flow or an employer flow, then use the matching tool group:

• Candidate: candidate.search_jobs, candidate.get_job,

candidate.apply_to_job, candidate.get_applications, candidate.accept_invitation / candidate.decline_invitation, candidate.withdraw_application, candidate.set_saved_job, candidate.get_saved_jobs.

• Employer: employer.create_job → employer.publish_job →

employer.close_job / employer.archive_job for the lifecycle; employer.search_candidates_for_job or employer.search_candidates_by_query for discovery; employer.invite_candidate, employer.list_applicants, employer.get_applicant_detail, employer.set_review_status for pipeline work.

Step 3: Explain matches with white-box data

When presenting employer search results, keep the tier structure (best/good/weak) and surface each candidate's matchExplanation: fit score, interview-proven skills, gaps, and rationale. For deeper comparison, fetch per-candidate interview evidence with employer.get_candidate_evidence and employer.get_applicant_transcript.

Examples

Example 1: Candidate job search

User: "Find me remote TypeScript jobs and apply to the best one."
Agent: candidate.search_jobs(query="TypeScript", remote=true)
       → present ranked results → candidate.get_job(id)
       → confirm with the user → candidate.apply_to_job(id)

Example 2: Employer candidate discovery

User: "Who are the best candidates for my Senior Backend role?"
Agent: employer.search_candidates_for_job(jobId)
       → report Best tier with each candidate's fit score, proven
         skills, and gaps → employer.invite_candidate on approval

Best Practices

• ✅ Confirm with the user before applying, inviting, or changing job

status — these are visible, stateful marketplace actions.

• ✅ Quote the white-box match explanation when recommending a candidate,

so the employer sees why, not just a score.

• ✅ Use request_access for key onboarding instead of asking users to

paste credentials into chat.

• ❌ Don't fabricate fit scores or ranks — only report what the tools

return.

• ❌ Don't apply to jobs or send invitations in bulk without explicit

user approval.

Limitations

• Requires a WorkorAI account and API key; tools fail without a valid key.
• This skill does not replace environment-specific validation, testing, or

expert review.

• Stop and ask for clarification if required inputs, permissions, or safety

boundaries are missing.

Security & Safety Notes

• All operations go through the remote WorkorAI MCP server over HTTPS; the

skill itself runs no shell commands.

• Mutating tools (apply, withdraw, invite, publish, close, delete) should

be preceded by an explicit user confirmation.

• Treat API keys as secrets: store them in MCP client configuration, never

in chat transcripts or committed files.

Additional Resources

• Source repository — full skill

with reference files and agents (npm: @workorai/agent-kit)

• WorkorAI MCP endpoint