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Greenhouse MCP

Greenhouse MCP

by benmonopoli
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

search_candidates_by_name

Find candidates by name in Greenhouse using case-insensitive substring matching. Use returned candidate IDs to look up applications and start recruiting workflows.

Instructions

Find candidates by name. Read-only — the starting point for most workflows.

Users always refer to candidates by name, not ID. Use this first whenever a user mentions a candidate, then list_applications(candidate_id=...) to find their application IDs (candidate records don't include applications). Case-insensitive substring match — "Sarah" finds "Sarah Chen", "Sarah O'Brien", etc. Greenhouse has no name filter, so this scans up to max_pages × per_page candidates; if has_more is true, pass next_cursor to keep scanning.

Input Schema

NameRequiredDescriptionDefault
nameYesName to search — matches first, last or preferred name (case-insensitive substring)
cursorNoResume a scan: pass next_cursor from a previous response that had has_more=true
per_pageNoCandidates scanned per page (max 500)
max_pagesNoMaximum pages to scan before stopping

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.5.0
    • addedInput schema / properties / cursor
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Resume a scan: pass next_cursor from a previous response that had has_more=true",
+  "title": "Cursor"
+}
    • addedInput schema / properties / max_pages / description
      Added value: +"Maximum pages to scan before stopping"
    • addedInput schema / properties / name / description
      Added value: +"Name to search — matches first, last or preferred name (case-insensitive substring)"
    • addedInput schema / properties / per_page / description
      Added value: +"Candidates scanned per page (max 500)"
  2. Addedv0.3.3

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: declares read-only, discloses case-insensitive substring matching, warns that Greenhouse has no name filter so the tool scans up to max_pages × per_page candidates, and explains next_cursor resumption. This is exactly the cost/pagination behavior an agent needs before calling.

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?

Front-loads the purpose in the first sentence, then layers routing, matching semantics, and pagination in short, scannable sentences. No sentence is 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?

Covers identity, matching behavior, the downstream call, and pagination, and an output schema exists so return values need no explanation. Nothing required 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%, so the baseline is 3, but the description adds meaning beyond it by framing cursor/per_page/max_pages as a scan-and-paginate contract ('scans up to max_pages × per_page... pass next_cursor to keep scanning') rather than as isolated fields.

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 ('Find candidates by name') and immediately differentiates itself from list_candidates and search_candidates_by_email by anchoring on the name-based lookup that users actually perform. An agent can select this over its many siblings without opening any schema.

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?

Explicitly says to use this first whenever a user mentions a candidate, then chains to list_applications(candidate_id=...) because candidate records don't include applications. It gives the when, the ordering, and the next step — nothing is left to inference.

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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