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Glama

Greenhouse

by io.github.pipeworx-io

Greenhouse List Jobs

greenhouse_list_jobs
Read-onlyIdempotent

Browse open and closed job postings. Returns titles, IDs, departments, statuses, and posting dates. Use greenhouse_get_job for full details and candidate pipeline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (default 1)
statusNoFilter by status: open, closed, draft (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 postings with basic details

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 postings with basic details",
+      "items": {
+        "properties": {
+          "created_at": {
+            "description": "ISO date job was created",
+            "type": "string"
+          },
+          "department": {
+            "description": "Department name",
+            "type": "string"
+          },
+          "id": {
+            "description": "Job ID",
+            "type": "number"
+          },
+          "status": {
+            "description": "Job status (open, closed, draft)",
+            "type": "string"
+          },
+          "title": {
+            "description": "Job title",
+            "type": "string"
+          },
+          "updated_at": {
+            "description": "ISO date job was updated",
+            "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",
+    "per_page": 100,
+    "status": "open"
+  }
+]
  3. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by specifying the returned fields (titles, IDs, etc.), which gives the agent a concrete expectation of the output. No contradictions with annotations.

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, each adding essential information: the first states the purpose and scope, the second lists key output fields and directs to the detail tool. No redundant or extra words.

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 tool is a simple read-only list operation with an output schema, the description covers the main behavior and distinguishes from the detail sibling. It lacks explicit mention of pagination (e.g., 'use page and per_page to paginate') but the schema already covers parameters, making this a minor gap.

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%, and the schema descriptions for 'status', 'page', 'per_page', and '_apiKey' are clear. The description does not add new parameter-level detail beyond the schema; it only mentions the tool browses 'open and closed' postings, which aligns with the status parameter but adds no syntax or format guidance.

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 the tool browses open and closed job postings and enumerates returned fields (titles, IDs, departments, statuses, posting dates). It explicitly distinguishes from the sibling tool greenhouse_get_job, which retrieves full details and candidate pipeline.

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 directs users to greenhouse_get_job for 'full details and candidate pipeline,' implying this tool is for high-level listing. It provides clear context on when to use the listing tool vs. the detail tool, though it does not explicitly state when not to use it or mention alternatives beyond the single sibling.

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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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