Skip to main content
Glama

Greenhouse

by io.github.pipeworx-io

Greenhouse List Candidates

greenhouse_list_candidates
Read-onlyIdempotent

Search candidates in your ATS. Returns names, IDs, email addresses, and application status. Use greenhouse_get_candidate for full profile details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (default 1)
_apiKeyYesGreenhouse Harvest API key
per_pageNoResults per page (max 500, default 50)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of items returned.
itemsYesList of candidates with basic profile information

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "count": {
+      "description": "Number of items returned.",
+      "type": "integer"
+    },
+    "items": {
+      "description": "List of candidates with basic profile information",
+      "items": {
+        "properties": {
+          "email": {
+            "description": "Candidate email address",
+            "type": "string"
+          },
+          "first_name": {
+            "description": "Candidate first name",
+            "type": "string"
+          },
+          "id": {
+            "description": "Candidate ID",
+            "type": "number"
+          },
+          "last_name": {
+            "description": "Candidate last name",
+            "type": "string"
+          },
+          "status": {
+            "description": "Application status",
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "items",
+    "count"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-greenhouse-api-key"
+  },
+  {
+    "_apiKey": "your-greenhouse-api-key",
+    "page": 2,
+    "per_page": 100
+  }
+]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by stating the tool returns specific fields (names, IDs, etc.) and suggests an alternative for more detail. No contradictions.

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?

Two sentences: first states purpose and return values, second provides actionable guidance. No filler words, front-loaded with key 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?

Given that the tool has an output schema and 100% parameter coverage, the description succinctly covers what the tool returns and how to get more details. Pagination parameters are documented in the schema. The description is complete for a paginated list tool with good annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all three parameters (page, _apiKey, per_page). The description does not add new meaning beyond the schema, meeting the baseline for high coverage.

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 clearly states 'Search candidates in your ATS' with a specific verb and resource. It lists returned fields (names, IDs, email addresses, application status) and distinguishes from sibling tool greenhouse_get_candidate.

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 explicitly recommends greenhouse_get_candidate for full profile details, indicating when not to use this tool. It provides a clear alternative within the same family, though it lacks explicit guidance on when to use this vs. other list tools like greenhouse_list_applications.

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

All Greenhouse toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions, but some overlap exists between research tools like ask_pipeworx, deep_research, and bet_research, which could confuse an agent. The Greenhouse-specific tools are clearly separated by the 'greenhouse_' prefix, aiding disambiguation.

Naming Consistency3/5

Tool names follow snake_case but vary in style: some have a prefix like 'greenhouse_' or 'pipeworx_', others do not (e.g., ask_pipeworx vs. deep_research). The verb-object pattern is inconsistent (e.g., 'generate_llms_txt' vs. 'entity_profile'), making naming less predictable.

Tool Count2/5

35 tools is excessive for a single server, especially one named 'Greenhouse' which implies an ATS focus. The set aggregates multiple domains (ATS, data research, memory, prediction markets) without clear scoping, overwhelming the agent and reducing coherence.

Completeness3/5

The Pipeworx/data research subset is fairly complete with lookups, comparisons, verification, and subscriptions. However, the Greenhouse ATS subset lacks create/update/delete operations, leaving notable gaps. The mixed domains make overall completeness uneven.

Resources