Get the user's persona: inferred job-search preferences and traits with an understanding score. Use it to tailor advice and searches.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| meta | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare that the tool is read-only, idempotent, and non-destructive, so the safety profile is clear. The description adds light value by noting the persona is 'inferred' and includes an understanding score, but it does not disclose further behavioral details beyond what the annotations and output schema would provide.
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?
Two short sentences deliver purpose, content, and usage guidance with no filler. The core statement is front-loaded and every word contributes meaning.
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 zero-parameter, read-only lookup tool with an output schema available, the description provides sufficient guidance on what the tool does and how the result should be used. Nothing important is missing for an agent to invoke it correctly.
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 and the input schema is empty, so there are no parameter semantics to explain. The baseline for a zero-parameter tool is 4, and the description does not need to compensate for any schema gaps.
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 uses a specific verb ('Get') with a clear resource ('the user's persona') and explains what the persona consists of: inferred job-search preferences and traits with an understanding score. This makes the tool's purpose immediately recognizable and naturally distinct from sibling tools like persona.confirm_trait or persona.correct_trait.
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 clear usage context by saying 'Use it to tailor advice and searches,' which tells the agent when the resulting persona data should be applied. It does not explicitly mention alternatives or when not to use it, but the intended call context is evident enough.
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.
The tools are largely distinct due to the domain-prefixed naming (inbox, jobs, signals, etc.) and detailed descriptions. While there is some overlap among inbox actions like acknowledge, apply_suggestion, and decline, the descriptions clarify each behavior. The signals and recommendations sub-groups also have clear boundaries, making misselection unlikely.
All tools follow a consistent pattern of domain.entity.action or domain.action (e.g., inbox.list, jobs.interviews.add, signals.recommendations.dismiss). This uniform camelCase-with-dots convention makes the set highly predictable and easy to navigate.
At 55 tools, the server far exceeds the 25-tool threshold for 'too many' as per the calibration. While the broad domain of job search management justifies numerous operations, the count is still overwhelming and could overwhelm agents or cause selection errors. Several signal-related tools could potentially be consolidated without compromising functionality.
The tool set covers the full lifecycle of job applications: inbox management (list, get, draft, send), job tracking (add, update, archive, delete), interviews (add, update, delete), offers (create, update, accept, decline, negotiation), and company signals (track, pause, recommend, block). There are no obvious missing operations for the core workflows.
