open-greenhouse-mcp

bulk_reject

Reject multiple job applications in a single call by supplying application IDs, with optional rejection email and reason. Use after identifying candidates via stale_applications or pipeline_summary for efficient bulk processing.

Instructions

Reject multiple applications in one call. Write operation — rate-limited.

Users say "reject everyone who's been inactive for 30 days on the Backend role." First use stale_applications to identify the targets, then pass their application_ids here. For rejection_reason_id: list_rejection_reasons → match by name. Processes sequentially with rate-limit delays.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "application_ids": "<application_ids>"
    },
    "name": "bulk_reject"
  }
}

Input Schema

Name	Required	Description
`application_ids`	Yes	Application IDs to reject — get from list_applications or pipeline_summary
`rejection_email`	No	Send rejection email to each candidate
`rejection_reason_id`	No	Rejection reason ID — get from list_rejection_reasons

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.5.0Aug 1, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that this is a 'Write operation — rate-limited' and that it 'Processes sequentially with rate-limit delays.' This adds useful behavioral context beyond the schema, though it does not mention reversibility or other side effects like email sending (which is parameterized).

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 compact paragraph with a clear front-loaded first sentence. The user example and workflow are relevant, but the text is somewhat dense and could be structured with bullets or shorthand. Still, every sentence adds value.

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 complexity (batch write operation, 3 params, output schema), the description is fairly complete. It covers the use case, prerequisites, and rate-limit behavior. Minor omissions like maximum batch size or error handling are not described, but the output schema likely sufficient.

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?

The schema already describes all parameters (100% coverage), so the baseline is 3. The description adds extra semantics by describing how to obtain application_ids via stale_applications and how to match rejection_reason_id by name using list_rejection_reasons, which enriches the parameter guidance.

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 explicitly states 'Reject multiple applications in one call,' which is a specific verb (reject), resource (applications), and scope (multiple in one call). This clearly distinguishes it from the sibling reject_application, which handles a single application.

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 provides a concrete usage example: 'First use stale_applications to identify the targets, then pass their application_ids here.' It also advises matching rejection_reason_id via list_rejection_reasons, giving a clear workflow. However, it does not explicitly state when not to use this tool (e.g., for a single rejection), so it lacks full exclusion guidance.

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