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Comradery64

open-greenhouse-mcp

by Comradery64

list_candidates

Retrieve candidate records using filters for date ranges, IDs, and pagination, enabling bulk data operations in Greenhouse.

Instructions

List candidates with optional filters. Read-only.

For finding a specific person, use search_candidates_by_name (by name) or search_candidates_by_email (by email) — faster and simpler. Use this tool for bulk operations: date-range queries, fetching by specific IDs, or paginating through the full database.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (starts at 1)
emailNoFilter by exact email address
paginateNo'single' for one page, 'all' to auto-fetch every pagesingle
per_pageNoResults per page (max 500)
candidate_idsNoFilter to specific candidate IDs
created_afterNoISO 8601 datetime — only candidates created after this
updated_afterNoISO 8601 datetime — only candidates updated after this
created_beforeNoISO 8601 datetime — only candidates created before this
updated_beforeNoISO 8601 datetime — only candidates updated before this

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description explicitly states 'Read-only' and mentions pagination through the full database, which gives the agent a clear expectation of safe, potentially high-volume behavior. With no annotations provided, this disclosure carries the full burden and is adequate, though it does not detail rate limits or default return 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?

Two short paragraphs: the first states purpose and safety in one sentence, the second provides usage guidance. Every sentence earns its place with no redundancy or fluff.

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?

For a list tool with 9 parameters and an output schema, the description covers purpose, safety, alternatives, and use-case scenarios. It doesn't need to explain return values because an output schema exists, and the schema covers parameter details.

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 parameters are individually documented. The description adds semantic grouping — 'date-range queries, fetching by specific IDs, or paginating' — which maps to created_after/updated_after, candidate_ids, and page/per_page, helping the agent choose the right filters for a task.

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 opens with 'List candidates with optional filters' — a specific verb+resource statement. It explicitly distinguishes this tool from search_candidates_by_name and search_candidates_by_email, positioning it for bulk operations like date-range queries and ID filtering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: 'For finding a specific person, use search_candidates_by_name... or search_candidates_by_email... faster and simpler. Use this tool for bulk operations...' This names alternatives and defines the exact use cases, making the when-to-use decision unambiguous.

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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