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Greenhouse MCP

Greenhouse MCP

by benmonopoli
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

disable_user

Deactivate a Greenhouse user by user_id to prevent login. Use list_users to find the ID; reverse with enable_user.

Instructions

Deactivate (disable) a Greenhouse user, preventing login. Write operation.

Users say "deactivate John's account." To find user_id: list_users → match by name or email. Can be reversed with enable_user. Returns {"success": true} on success.

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": {
      "user_id": "<user_id>"
    },
    "name": "disable_user"
  }
}

Input Schema

NameRequiredDescriptionDefault
user_idYesGreenhouse user ID to disable

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.0
    • addedInput schema / properties / user_id / description
      Added value: +"Greenhouse user ID to disable"
  2. Addedv0.3.4

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 full burden, and it does disclose that this is a mutation, that it is reversible via enable_user, and that it returns {"success": true}. It stops short of stating required permission level or side effects beyond login blocking (e.g. session termination, notification), so it is strong but not exhaustive.

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?

Very short and front-loaded: purpose first, then lookup workflow, then reversibility and return shape. No filler sentences; every line carries usable information.

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 one-parameter mutation with an output schema, the description covers purpose, effect, ID discovery, reversibility, and a return summary. Nothing an agent needs to invoke it correctly is missing.

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 100% and the single parameter is documented as "Greenhouse user ID to disable", so the baseline is 3. The description goes beyond the schema by explaining how to obtain the ID (list_users → match by name or email), which is genuine added value for the one parameter.

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?

States a specific verb+resource ("Deactivate (disable) a Greenhouse user") plus the concrete effect ("preventing login"), and labels it a write operation. This clearly distinguishes it from siblings like enable_user and the various list/get user tools.

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 a natural-language trigger ("deactivate John's account"), an explicit prerequisite workflow for obtaining user_id (list_users → match by name or email), and names the reversing tool (enable_user). The agent is routed to the correct alternatives rather than having to infer them.

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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