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Greenhouse MCP

Greenhouse MCP

by benmonopoli
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_eeoc_for_application

Retrieve EEOC demographic data for a specific job application to support compliance reporting and diversity tracking.

Instructions

Get EEOC data for a specific application. Read-only.

To find application_id: search_candidates_by_name → list_applications(candidate_id=...) → match the application to the job.

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": {
      "application_id": "<application_id>"
    },
    "name": "get_eeoc_for_application"
  }
}

Input Schema

NameRequiredDescriptionDefault
application_idYesGreenhouse application ID

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.0
    • addedInput schema / properties / application_id / description
      Added value: +"Greenhouse application ID"
  2. Addedv0.3.4

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the key behavioral trait ('Read-only'), which is good, but says nothing about what EEOC data contains, permissions, or rate limits. Adequate but not rich.

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 short lines, front-loaded with purpose followed by the parameter-discovery workflow. No wasted words and everything earns its place.

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?

With an output schema present, return values need not be explained, and the single-param getter is well covered. The only gap is lack of differentiation from the sibling list_eeoc.

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 schema already documents application_id as the Greenhouse application ID (baseline 3). The description adds real value by explaining the multi-step workflow to obtain that ID, exceeding the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Get EEOC data for a specific application'), so the agent knows exactly what it returns. It does not, however, distinguish itself from the sibling list_eeoc, leaving the boundary between the two to inference.

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 gives a concrete discovery path for the required application_id (search_candidates_by_name -> list_applications -> match to job), which is genuinely useful. It offers no explicit when-to-use vs. when-not guidance, e.g., how it differs from list_eeoc or get_application.

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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