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Greenhouse

by io.github.pipeworx-io

Greenhouse List Applications

greenhouse_list_applications
Read-onlyIdempotent

View job applications across your pipeline. Returns applicant names, job IDs, application status, and submission dates. Filter by job or stage (e.g., 'screening', 'interview').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (default 1)
job_idNoFilter by job ID (optional)
statusNoFilter by status: active, converted, hired, rejected (optional)
_apiKeyYesGreenhouse Harvest API key
per_pageNoResults per page (max 500, default 50)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of items returned.
itemsYesList of job applications across pipeline

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 job applications across pipeline",
+      "items": {
+        "properties": {
+          "applied_at": {
+            "description": "ISO date application was submitted",
+            "type": "string"
+          },
+          "candidate": {
+            "properties": {
+              "first_name": {
+                "description": "Candidate first name",
+                "type": "string"
+              },
+              "last_name": {
+                "description": "Candidate last name",
+                "type": "string"
+              }
+            },
+            "type": "object"
+          },
+          "candidate_id": {
+            "description": "Candidate ID",
+            "type": "number"
+          },
+          "id": {
+            "description": "Application ID",
+            "type": "number"
+          },
+          "job_id": {
+            "description": "Job ID",
+            "type": "number"
+          },
+          "stage": {
+            "description": "Current pipeline stage (e.g., screening, interview)",
+            "type": "string"
+          },
+          "status": {
+            "description": "Application status (active, converted, hired, rejected)",
+            "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",
+    "job_id": 54321,
+    "status": "active"
+  }
+]
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds value by detailing returned fields (names, status, dates), which helps the agent understand output without deep schema inspection. 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.

Conciseness4/5

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

Two sentences clearly state purpose and key features. Could be slightly more structured but is efficient and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (mentioned), the description adequately covers what the tool does and what data it returns. Filtering is mentioned, but pagination is implied via schema. No critical gaps.

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 baseline is 3. The description enriches parameters by providing examples for status ('screening', 'interview'), which adds clarity beyond the schema's generic descriptions. However, it does not elaborate on page or per_page usage.

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 explicitly states it lists job applications, returns specific fields (applicant names, job IDs, status, dates), and supports filtering. It clearly distinguishes from siblings like greenhouse_list_candidates and greenhouse_list_jobs.

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 mentions filtering by job or stage, but does not explicitly explain when to use this tool vs. other list tools (e.g., list_candidates) or provide exclusions. Context implies it's for applications, but lacks direct guidance.

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

All Greenhouse toolsInspect
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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