Greenhouse MCP

anonymize_candidate

Scrub a candidate's personal data for GDPR compliance by anonymizing specified fields while preserving their hiring record for reporting. This action is irreversible.

Instructions

Anonymize a candidate's personal data for GDPR/privacy compliance. Write operation.

Scrubs the listed data groups (name, emails, phones, attachments, notes, etc.) while preserving the record and hiring activity for reporting. Irreversible; large scopes complete asynchronously. Greenhouse requires an explicit list of fields — confirm with the user what to remove. To get the candidate_id: search_candidates_by_name → get_candidate.

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": {
      "candidate_id": "<candidate_id>",
      "fields": "<fields>"
    },
    "name": "anonymize_candidate"
  }
}

Input Schema

Name	Required	Description	Default
`fields`	Yes	Data groups to scrub (at least one), e.g. ['full_name', 'email_addresses', 'phone_numbers', 'addresses', 'attachments', 'notes', 'education', 'employment', 'scorecards_and_interviews', 'offers']
`candidate_id`	Yes	Greenhouse candidate ID

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed7 schema fields changedv0.5.0Oct 5, 2026

addedInput schema / properties / candidate_id / description
```
Added value: +"Greenhouse candidate ID"
```

removedInput schema / properties / fields / anyOf

Removed value: -[
-  {
-    "items": {
-      "type": "string"
-    },
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]

removedInput schema / properties / fields / default
```
Removed value: -null
```

addedInput schema / properties / fields / description

Added value: +"Data groups to scrub (at least one), e.g. ['full_name', 'email_addresses', 'phone_numbers', 'addresses', 'attachments', 'notes', 'education', 'employment', 'scorecards_and_interviews', 'offers']"

addedInput schema / properties / fields / items
```
Added value: +{
+  "type": "string"
+}
```
addedInput schema / properties / fields / type
```
Added value: +"array"
```

changedInput schema / required

Previous value: -[
-  "candidate_id"
-]New value: +[
+  "candidate_id",
+  "fields"
+]

Addedv0.3.4Apr 15, 2026

TDQS

A4.5/5.0

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 does well: it declares a write operation, irreversibility, asynchronous completion for large scopes, and the requirement for an explicit field list. It stops short of stating permission/auth requirements or rate limits.

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?

Purpose is front-loaded in the first sentence, followed by the critical constraints (irreversible, async, explicit fields) in order of importance. Every sentence carries operational weight with no filler.

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 two-parameter destructive mutation with an output schema present, the description covers everything an agent needs to act correctly: what gets scrubbed, what is preserved, reversibility, async behavior, and how to source the ID.

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%, so both parameters are already documented and the field list is enumerated in the schema. The description still adds value by warning that Greenhouse requires an explicit field list, advising confirmation with the user, and showing the search_candidates_by_name → get_candidate path for candidate_id.

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 states a specific verb+resource ('Anonymize a candidate's personal data') with an explicit compliance motivation (GDPR/privacy). It distinguishes itself from the sibling delete_candidate by stating it preserves the record and hiring activity for reporting.

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?

It gives clear context (GDPR/privacy compliance, the need to confirm scope with the user) and a navigation path to obtain candidate_id. It does not explicitly name delete_candidate as the alternative for a hard removal, leaving that contrast to inference.

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

Deploy Server