open-greenhouse-mcp

reject_application

Reject a candidate for a job in Greenhouse ATS by application ID. Optionally record a rejection reason, add internal notes, and send a candidate email.

Instructions

Reject a candidate from a job. Write operation.

Users say "reject Sarah from the Backend role." To get the application_id: search_candidates_by_name → get_candidate → match the application to the job. For rejection_reason_id: list_rejection_reasons → match by name. For email templates: list_email_templates. Can be reversed with unreject_application. For bulk rejections, use bulk_reject.

Input Schema

TableJSON Schema

Name	Required	Description
`notes`	No	Internal rejection notes (not sent to candidate)
`application_id`	Yes	Greenhouse application ID
`rejection_email`	No	Optional email to candidate: {email_template_id, send_email_at (ISO 8601)}
`rejection_reason_id`	No	Reason ID — get from list_rejection_reasons

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.5.0Aug 1, 2026

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations, the description carries the burden of disclosure. It clearly states 'Write operation' and notes reversibility via unreject_application, but does not detail side effects like email sending or permission requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: first line states purpose, then provides concrete usage context. Every sentence earns its place with no repetition or filler.

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

Completeness4/5

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

Given the tool's moderate complexity and presence of an output schema, the description covers the main workflow, reversibility, and alternatives. It could have mentioned prerequisites like stage constraints, but the current coverage is strong.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds real value by explaining how to source parameters (application_id via search_candidates_by_name → get_candidate, rejection_reason_id via list_rejection_reasons, email template via list_email_templates), going well beyond the baseline.

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 uses a specific verb (reject) and resource (candidate from a job), and explicitly labels it as a write operation. It also distinguishes itself from related tools like unreject_application and bulk_reject.

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

Usage Guidelines5/5

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

Provides explicit when-to-use guidance, including a detailed workflow to obtain application_id and rejection_reason_id, and names alternatives (unreject_application for reversal, bulk_reject for bulk operations).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.