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jobctl

by io.github.DmytroRybka

Log interview

log_interview

Log an interview for an application (max 10 per application): when it takes place, its status (planned or done), who attends (each with an optional profile link), related links (prep materials the company sent, meeting URL), and a short recap of what was discussed. Also keeps the application's next-interview date in sync. Example: log_interview({application_id: "…", scheduled_at: "2026-07-20T14:00:00+02:00", status: "planned", attendees: [{name: "Anna Weber (HR)", url: "https://linkedin.com/in/annaweber"}], links: [{label: "Prep materials", url: "https://company.com/interview-prep"}], recap: "System design round"}).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linksNoRelated links: prep materials, meeting URL, docs
recapNoShort recap of what was discussed
statusNoplanned (default) or done
attendeesNoWho attends, each with an optional profile link
scheduled_atYesISO datetime with offset, e.g. 2026-07-20T14:00:00+02:00
application_idYes

TDQS

A4.4/5.0
Behavior4/5

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

Annotations (readOnlyHint=false, destructiveHint=false) indicate a non-destructive write operation. The description adds behavioral context by noting the 10-per-application limit and the syncing of next-interview date. No contradiction with annotations, and the additional details are helpful for understanding side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear lead sentence, followed by parameter details and an example. It covers all essential information without verbosity. The example is compact and illustrative. Every sentence contributes to understanding the tool's function and constraints.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 6 parameters (83% schema coverage), no output schema, and moderate complexity, the description comprehensively covers inputs, constraints (max 10), side effects (date sync), and provides a concrete example. An agent can correctly invoke the tool based on this description alone.

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 coverage is 83% with descriptions for most parameters. The description adds meaning by explaining links as 'prep materials' or 'meeting URL', attendees as 'who attends with profile link', and recap as 'short recap'. The example further clarifies parameter usage, adding 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?

The description clearly states it logs an interview for an application, specifying key fields (scheduled_at, status, attendees, links, recap) and a constraint (max 10 per application). It also mentions a side effect of syncing next-interview date, making the tool's purpose unmistakable. This distinguishes it from sibling tools like add_application or add_note.

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?

The description implies usage context by detailing what the tool does and its limit (max 10 per application). It provides an example that demonstrates typical use. While it does not explicitly state when to avoid this tool or mention alternatives, the context is clear enough for an agent to decide.

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

A4.4/5.0
Disambiguation5/5

Every tool targets a distinct resource and action: application, note, analysis attachment, file attachment, interview file, application detail, due actions, planned schedule, application listing, interview logging, and application updates. Even the three 'attach' tools are clearly differentiated by purpose (analysis vs CV/cover letter vs interview prep). The only potential overlap between get_due and get_planned is explicitly resolved in the descriptions.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case (add_application, get_application, list_applications, update_application, attach_analysis, log_interview, etc.). The verb prefix accurately reflects the action, and nouns are singular for single-item operations and plural for list operations, maintaining a predictable convention.

Tool Count5/5

With 11 tools, the server is well-scoped for a job application tracking domain. Each tool covers a necessary function without redundancy, and the count is neither sparse nor overwhelming.

Completeness4/5

The tool set covers the full lifecycle of an application: create, read, list, update, plus notes, attachments, analysis, and interview logging. Minor gaps exist—there is no explicit delete or archive tool (though status updates can archive), and interviews cannot be directly updated or removed after logging—but these are workable within the existing surface.

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