На каких условиях кандидат готов выйти: зарплатные ожидания (base, бонус, equity, deal-breakers), срок выхода, формат работы, гео/таймзона, виза, релокация. lang: ru (по умолчанию) или en. / Candidate's terms: compensation expectations, notice period, work format, location, visa.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Язык: ru или en | |
| candidate | No | Slug кандидата. В личном режиме не обязательно: отправьте null или не передавайте параметр. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden of behavioral disclosure. It discloses the scope of the returned data (salary components, notice period, work format, visa, relocation) and the language default, but it does not mention side effects, error behavior, or permissions. The 'get' verb implies a read operation, but that is not explicitly stated.
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 and front-loads the tool's purpose with a concrete list of returned terms. The bilingual duplication is slightly redundant but still efficient and unlikely to confuse an agent.
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 the tool's low complexity, full schema coverage, and simple 2-parameter input, the description sufficiently explains what data is returned and acknowledges language options. It is not fully complete because it lacks explicit usage guidance and behavioral notes, but nothing critical is missing for basic invocation.
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 description coverage is 100%, so the schema already documents both lang and candidate. The description repeats the lang default and adds the candidate slug context only indirectly; it does not add meaningful semantics beyond the schema.
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 this tool retrieves the candidate's terms for joining: compensation expectations, notice period, work format, location, visa, and relocation. This specific field list distinguishes it from sibling tools like get_profile or get_resume, so an agent can tell them apart without opening schemas.
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 usage context is implied by the content: use this when you need a candidate's employment conditions such as salary expectations or notice period. However, it does not explicitly state when to prefer this over alternatives or provide exclusions, leaving the agent to infer routing from the sibling list.
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.
Each tool targets a distinct candidate resource or workflow step: profile, resume, conditions, contacts, FAQ, references, interview, assessment, vacancy matching, and messaging are clearly separated. Even similar get_* tools are differentiated by description, and ask_about_experience is explicitly a targeted query rather than a full document.
Most tools follow a clear imperative get_/send_/manage_/match_/message_ pattern, making the set predictable. The exceptions are 'assessment' and 'recruiter_context', which are noun-only names and break the verb-led convention, though the lowercase snake_case style is consistent throughout.
Thirteen tools is well within the ideal range for a recruiting-focused server. Each tool covers a meaningful step in the candidate engagement workflow, from initial context and profile retrieval to vacancy sending, matching, interviewing, and assessment, with no obvious bloat.
The core candidate lifecycle is well covered: viewing candidate information, contacting them, sending vacancies, matching against job descriptions, proposing interviews, and managing assessments. Minor gaps exist around post-assessment progression, explicit offer/rejection handling, and broader pipeline or status management, but agents can mostly work around these.