List all your messaging conversations with last message preview. Shows conversation status (awaiting reply, new reply, read). Requires employer authentication.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does state a prerequisite ('Requires employer authentication') and what the output includes (preview and status), which is useful. However, it does not explicitly confirm this is a read-only operation, nor does it mention potential side effects, pagination, or return structure. It provides some transparency but leaves notable gaps.
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 compact, consisting of two sentences that lead with the primary action ('List all your messaging conversations') and follow with relevant details (preview, status, authentication). Every sentence contributes to agent selection and execution. There is no redundancy or extraneous information, striking an ideal balance between completeness and brevity.
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?
For a parameterless list operation, the description covers the essential aspects: what is listed, what is shown for each conversation, and the authentication requirement. It lacks explicit details about return format, sorting, or pagination, but since there is no output schema and the tool is simple, this is a minor shortcoming. The description is adequate for an agent to understand and invoke the tool correctly, though a note about potential volume or ordering would have made it fully complete.
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?
The tool has zero parameters, so the schema is trivial and provides no information to clarify. The baseline for 0 parameters is 4, and the description appropriately focuses on the tool's behavior rather than parameter details. It adds contextual value by explaining what the list contains, which indirectly informs parameter usage. No further parameter documentation is needed.
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 'conversations', and adds specifics: 'with last message preview' and 'Shows conversation status'. This distinguishes it from sibling tools like get_conversation, which retrieves a single conversation. The purpose is unambiguous and immediately actionable.
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 explains what the tool does but does not provide explicit guidance on when to choose it over alternatives. It does not mention, for example, that get_conversation should be used for a single conversation or that start_conversation is for creating new ones. An agent must infer usage from the action alone, which is insufficient for tools with many messaging-related siblings.
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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Each tool targets a distinct resource and action: job posting vs job browsing vs job management vs company management vs talent search vs profile editing vs messaging vs application tracking. Even similar tools like get_companies and search_companies are clearly differentiated by purpose and parameters. Overlapping concepts (e.g., post_job_public vs create_company_job) have explicit differences in authentication and cost.
All tools use snake_case and follow a verb-first pattern (add_, get_, create_, update_, delete_, search_, list_, send_, etc.). There are minor deviations like 'show_company_job' instead of 'get_company_job' and 'mark_message_read' which is a verb+noun+adjective, but the overall style is consistent and predictable across the 41 tools.
With 41 tools, this is well into the 'too many' range (25+). While the breadth reflects a comprehensive jobs platform, the number is excessive for an agent to efficiently navigate. Many tools could be consolidated (e.g., profile management could merge add_education/add_experience/update_profile, or company perks could be combined with profile updates). The tool count detracts from usability.
The tool set covers the full lifecycle: job posting (create, update, delete, list), job discovery (browse, search, related), company management (profile, perks, tech stack), talent search and messaging, application tracking (save, get, remove, update status), and data analytics (salary, statistics). Minor gaps exist—no delete/update for education or experience, no explicit 'close job' action—but these are edge cases and agents can work around them.