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100Hires - AI ATS & Recruitment Software

by com.100hires

hires_list_messages

Read-only

List messages sent or scheduled from a specific mail account. Returns outbound messages only (sent and scheduled), not received. Useful for monitoring cold outreach campaigns — check pending queue, delivery history, and plan next sends. Recommended size <= 10: messages include full HTML body; if the response exceeds the budget the tool returns isError:true with error_code=response_too_large and retry hints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (1-based). Default: 1.
sizeNoNumber of items per page (1-100). Default: 20.
viewNoResponse shape. Default `summary` excludes the HTML body and attachments metadata — recommended for list operations. Use `full` only when message body content is needed.summary
statusNoFilter by message status: `scheduled` (pending send), `sent` (delivered), `all` (both). Default: `all`.
date_toNoEnd of period (unix timestamp, seconds). Filters on scheduled/sent time.
date_fromNoStart of period (unix timestamp, seconds). Filters on scheduled/sent time.
from_account_idYesID of the mail account (from `GET /companies/mail-accounts` or `GET /users/{user_id}/mail-accounts`).

TDQS

A4.4/5.0
Behavior4/5

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

Beyond annotations (readOnlyHint, destructiveHint), description warns about large responses causing isError with error_code=response_too_large and retry hints, and explains that it returns only outbound messages. Adds meaningful behavioral details.

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?

Three sentences, front-loaded with purpose, no redundant information. Every sentence adds value: purpose, use case, and behavioral warning. Highly efficient.

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?

Covers pagination, view, status, date range, and required parameter. Warns about response size limit. Without output schema, some return format details are missing, but the description is sufficient for an agent to use the tool correctly in most cases.

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 covers all 7 parameters with descriptions. Description adds extra guidance: recommends size <=10, explains view enum options with examples, and clarifies date filter as unix timestamps. Provides value beyond 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?

Description explicitly states it lists outbound messages from a specific mail account, distinguishes from received messages, and gives concrete use cases like monitoring cold outreach. This provides a specific verb+resource scope that differentiates from sibling tools like list_candidate_messages.

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?

Provides context on when to use (monitoring cold outreach, checking pending queue) and a specific recommendation (size <= 10). Does not explicitly state when not to use or name alternatives, but the context is clear enough for an agent.

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

A3.7/5.0
Disambiguation4/5

Most tools have clearly distinct purposes due to specific entity and action combinations. However, with 133 tools, there is some overlap (e.g., multiple ways to move applications) and similar-sounding operations (e.g., batch_remove_tags vs remove_candidate_tag) that could cause confusion. The detailed descriptions help but the sheer number increases ambiguity.

Naming Consistency5/5

All tools follow a consistent 'hires_verb_noun' pattern with snake_case. Verbs are descriptive (create, list, get, delete, update, batch) and nouns match the domain entities (candidate, application, job, etc.). No mixing of conventions like camelCase or inconsistent verb styles.

Tool Count2/5

133 tools is excessive for a typical server scope. While a full-featured ATS requires many operations, this count suggests insufficient aggregation. Tools for similar entities (e.g., multiple update/delete variants) could be consolidated. The high number overwhelms the tool surface and increases complexity.

Completeness4/5

The tool set covers core CRUD operations for major entities (candidates, applications, jobs, companies, users, messages, forms, etc.) plus batch operations, webhooks, and advanced features like AI scoring and nurture campaigns. Minor gaps exist (e.g., no direct reporting/analytics tools), but most workflows can be executed.

Resources