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Greenhouse MCP

Greenhouse MCP

by benmonopoli
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_email_template

Retrieve an email template by ID to get its name, subject, body, email type, and sender settings. Find the ID with list_email_templates by matching the name.

Instructions

Get an email template by ID. Read-only.

Returns template name, subject, body or html_body, email_type and sender settings. To find template_id: list_email_templates → match by name.

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": {
      "email_template_id": "<email_template_id>"
    },
    "name": "get_email_template"
  }
}

Input Schema

NameRequiredDescriptionDefault
email_template_idYesEmail template ID — get from list_email_templates

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.0
    • addedInput schema / properties / email_template_id / description
      Added value: +"Email template ID — get from list_email_templates"
  2. Addedv0.3.4

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden, and it does declare 'Read-only' plus the returned fields (name, subject, body/html_body, email_type, sender settings). It does not cover auth requirements or error behavior (e.g. invalid ID), which is the main remaining gap.

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?

Purpose is front-loaded in the first sentence, followed by behavior and then the ID-discovery hint. Three short, purposeful lines with essentially no waste.

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?

For a one-parameter read tool with an output schema already describing return values, the definition covers purpose, safety, and ID acquisition. It is essentially complete, with only minor omissions like error/not-found behavior.

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 description coverage is 100%, so the single parameter is already documented in the schema. The description's guidance ('get from list_email_templates') largely duplicates the schema description, so it adds little beyond the baseline.

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 and resource ('Get an email template by ID') and implicitly separates itself from the sibling list_email_templates by emphasizing lookup by ID. An agent can distinguish this single-fetch tool from the listing tool without opening either schema.

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?

Explicitly tells the agent how to obtain the required ID ('list_email_templates → match by name'), which is effectively a routing instruction to the sibling. It lacks an explicit when-not-to-use clause, but the discovery workflow is clearly spelled out.

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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