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workorai

by io.github.work0r-ai

Invite candidate

employer.invite_candidate

Invite a discoverable candidate to one of the employer's PUBLISHED jobs. Creates a JobApplication with status=INVITED. If a prior WITHDRAWN row exists for this (candidate, job) pair, the row is UPDATEd back to INVITED (re-invite is allowed after the candidate withdrew on their own). INVITED, APPLIED, and DECLINED rows still block with INVITE_BLOCKED: INVITE_NOT_ALLOWED. Inspect existingApplications on employer.get_candidate before calling to know which case applies. Returns INVITE_BLOCKED with one of several sub-reasons (JOB_NOT_FOUND, JOB_NOT_PUBLISHED, CANDIDATE_NOT_FOUND, NOT_DISCOVERABLE, INVITE_NOT_ALLOWED) when the invite cannot be created. A missing vacancy and a vacancy owned by another employer both return JOB_NOT_FOUND (you cannot tell them apart — anti-enumeration).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYes
apiKeyNo
candidateUserIdYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
statusNo
applicationIdNo

TDQS

A4/5.0
Behavior5/5

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

With no annotations, the description fully discloses behaviors: creates/updates JobApplication, blocks on certain statuses, returns specific error sub-reasons, and anti-enumeration for missing jobs. This is comprehensive.

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 coherent paragraph that front-loads the main action and includes necessary details. It is concise without being overly lengthy.

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 complexity of edge cases, no annotations, and 0% schema coverage, the description covers return values, prerequisites, and error conditions thoroughly. It provides sufficient context for an AI agent to use the tool correctly.

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

Parameters2/5

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

Schema coverage is 0%, and the description only implicitly references jobId and candidateUserId in context. It does not explain the apiKey parameter or provide detailed semantics beyond parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action: inviting a discoverable candidate to a published job and creating a JobApplication with INVITED status. It is specific and covers the main purpose, though it does not explicitly differentiate from sibling tools.

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?

The description advises inspecting existingApplications before calling, and lists conditions that block the invite. It provides guidance on when to use the tool, including re-invite behavior, though it does not compare directly with sibling tools.

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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