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Greenhouse MCP

Greenhouse MCP

by benmonopoli

list_approvals_for_job

Retrieve all approval flows for a specific job, including approval type, status, and ordered approver groups. Use it to track who must approve a job or offer.

Instructions

List approval flows for a job. Read-only.

To find job_id: list_jobs → match by name. Each flow has approval_type (open_job, offer_job, offer_candidate), approval_status, sequential, requested_by_id, and approver_groups (ordered by sort_order), each with approvers (user_id, user_name, status: waiting/due/approved/rejected).

Input Schema

NameRequiredDescriptionDefault
job_idYesGreenhouse job ID

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.0
    • addedInput schema / properties / job_id / description
      Added value: +"Greenhouse job ID"
  2. Addedv0.3.4

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It states 'Read-only,' which is a key safety trait, but does not mention auth requirements, pagination behavior, or any other operational details. The detailed output structure is more about return values than 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.

Conciseness3/5

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

The first two sentences are essential and front-loaded. The final sentence detailing approval_type, approval_status, and approver_groups is redundant because an output schema exists, which makes the description longer than necessary for the information the agent actually needs from the text.

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?

For a simple list tool with one required parameter and an output schema, the description is complete enough. It covers purpose, read-only status, how to obtain the required job_id, and summarizes the output, so an agent has everything needed to invoke it correctly.

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 for job_id is 100%, so the baseline would be 3. The description adds value by explaining how to find job_id via 'list_jobs → match by name,' which is procedural guidance beyond the schema.

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 states a specific verb and resource: 'List approval flows for a job.' It is clear what the tool does. However, it does not explicitly differentiate itself from siblings like get_approval_flow or list_pending_approvals, leaving the agent to infer the distinction.

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

Usage Guidelines3/5

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

The description notes 'Read-only' and gives guidance on obtaining job_id via list_jobs, but it offers no explicit when-to-use vs alternatives, such as when to use this instead of list_pending_approvals or get_approval_flow. Usage is implied by the tool name and required job_id.

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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