List all mail accounts for all users in the current company. Use to resolve from_account_id before creating scheduled emails.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number (default 1) | |
| size | No | Page size (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that the tool lists all accounts for all users, implying broad scope and no destructive side effects. However, it does not disclose pagination behavior or any rate limits, which would add further transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences. The first sentence states the primary purpose, and the second adds actionable context. Every sentence earns its place without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool is a simple list operation with pagination, the description covers the scope (all users in the current company) and provides a use case. However, there is no output schema, and the description does not specify what fields each mail account object contains (e.g., id, email address, user). This omission slightly reduces completeness, but overall it is sufficient for an agent to understand when to call it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, meaning both parameters (page, size) are described in the schema. The description does not add any additional meaning beyond what the schema provides. Per the guidelines, the baseline score is 3 in this case.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and the resource 'mail accounts for all users in the current company'. It also provides a specific use case (resolving from_account_id before creating scheduled emails). However, it does not explicitly differentiate from similar sibling tools like hires_list_company_id_mail_accounts or hires_list_user_mail_accounts, which could cause ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a concrete context: use to resolve from_account_id before creating scheduled emails. However, it lacks explicit guidance on when not to use this tool or alternatives. For example, it does not mention that if you need mail accounts for a specific user, you might use hires_list_user_mail_accounts instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
