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workorai

by io.github.work0r-ai

Get candidate interview evidence

employer.get_candidate_evidence

Fetch the interview EVIDENCE (facts proven in the interview + their Q&A, the interview summary, the résumé summary, and GitHub/LinkedIn signals) for ONE candidate AGAINST one of your published jobs — the white-box basis to explain WHY a candidate ranks where they do. Use it AFTER search_candidates_for_job: shortlist with the scorecard, then read the evidence here for the few you care about and write your own comparative review. Returns NOT_FOUND if the job is missing / not yours / not published, or the candidate is not in that job's searchable pool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesOne of YOUR published vacancies — the evidence is scoped to it (must-linked facts use its required skills).
apiKeyNo
userIdYesA candidate from search_candidates_for_job for THIS jobId. The interview evidence to explain your ranking.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
evidenceNo

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses NOT_FOUND conditions and the content of evidence. It could mention more about idempotency or rate limits, but sufficiently covers behavioral traits.

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?

The description is a single well-structured sentence that front-loads the main action. It is efficient but slightly verbose in listing evidence components; could be slightly more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, the description need not explain return values. It covers preconditions, workflow, error conditions, and the purpose thoroughly. Complete for this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67%, but description adds meaning by explaining jobId must be a published vacancy and userId must come from search results. It also describes the returned evidence components, compensating for the missing apiKey description.

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?

The description clearly states it fetches interview evidence for one candidate against one job, and distinguishes it from siblings by referencing search_candidates_for_job and the white-box basis for ranking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says when to use (after search_candidates_for_job) and describes the workflow. However, it does not explicitly state when not to use this tool, so it's slightly less comprehensive.

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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TDQS

A3.8/5.0
Disambiguation5/5

Every tool in the candidate and employer sets targets a distinct action or resource with no ambiguity. Tools like `search_candidates_by_query` and `search_candidates_for_job` have clearly different purposes, and all other tools perform unique operations.

Naming Consistency5/5

All tools follow a consistent `domain.action` pattern with snake_case action names. The naming is uniform across both candidate and employer tools, using standard verbs like get, list, create, update, delete, search, set, etc.

Tool Count4/5

29 tools cover two distinct user roles (candidate and employer) with separate workflows. While above the typical 3-15 range, each tool serves a specific purpose and the count is justified for a hiring platform's API surface.

Completeness4/5

The tool surface provides comprehensive CRUD and lifecycle operations for jobs, applications, invitations, and candidate searches for both roles. Minor gaps like candidate profile update tools are likely handled outside the MCP server, so the set feels nearly complete.

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