Skip to main content
Glama
Sign In
Greenhouse MCP

Greenhouse MCP

by benmonopoli
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

source_effectiveness

Compare candidate sources by job or org-wide to see volume, active, rejected, hired, and hire rate; guide recruiting budget decisions.

Instructions

Which candidate sources produce the best results. Read-only.

Users say "which sources are working?" or "where should we spend recruiting budget?" Pass job_id (list_jobs → match by name) for one role, or omit for org-wide analysis. Returns volume, active, rejected, hired and hire rate per source, with each source's strategy (e.g. Referral).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "source_effectiveness"
  }
}

Input Schema

NameRequiredDescriptionDefault
job_idNoFilter to one job — list_jobs → match by name
created_afterNoISO 8601 date — only applications created after this

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.5.0
    • addedInput schema / properties / created_after / description
      Added value: +"ISO 8601 date — only applications created after this"
    • addedInput schema / properties / job_id / description
      Added value: +"Filter to one job — list_jobs → match by name"
  2. Addedv0.3.3

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does disclose the key trait explicitly: 'Read-only.' It also clarifies the scope switch (single role vs org-wide) and the returned metric shape. It omits permission requirements, rate limits, and result-size/pagination behavior.

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?

Front-loaded with the purpose, then read-only status, then usage triggers, then parameter guidance, then return shape — no filler sentences. The quoted user phrasings earn their place by aiding tool selection.

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?

An output schema exists, so return-value documentation is not strictly required, yet the description still summarizes the key fields. Combined with the read-only declaration, the job_id sourcing hint, and the scope toggle, an agent has everything needed to call this correctly.

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 already 100%, so the baseline is 3, but the description adds real meaning beyond the schema: it tells the agent to obtain job_id via list_jobs by name, and that omitting it switches to org-wide analysis — a semantic the schema's 'Filter to one job' note does not convey. created_after is left entirely to the schema.

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 states a specific analytical purpose — ranking candidate sources by results — and enumerates the exact metrics returned (volume, active, rejected, hired, hire rate, source strategy). That metric list clearly separates it from the sibling list_sources, which merely enumerates sources rather than measuring their performance.

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?

It gives concrete routing context, quoting the user questions it answers ('which sources are working?', 'where should we spend recruiting budget?') and explaining the job_id-present vs job_id-omitted modes. It never names an alternative tool to use instead (e.g. pipeline_metrics, list_sources), so the when-not guidance is absent.

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

Deploy Server

Other Tools