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Record My Screening Progress

record_my_application_outcome

Record one email-verified candidate's explicitly authorized, fixed-taxonomy application outcome for $0. The receipt is candidate-reported, not employer-verified, creates no acceptance-rate claim, and never changes organic ranking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
outcomeYes
application_idYesApplication ID returned by apply_to_job.
principal_authorizedYesTrue only after the named candidate authorizes this exact self-report.

TDQS

A4/5.0
Behavior4/5

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

Since there are no annotations, the description carries the full burden and does well: it discloses that the record is candidate-reported (not employer-verified), creates no acceptance-rate claim, and never changes organic ranking. It also mentions the $0 cost and explicit authorization requirement. This goes beyond simple 'record' and gives meaningful behavioral caveats, though it doesn't detail mutation semantics (e.g., idempotency, overwriting).

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

Conciseness5/5

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

Two sentences, front-loaded with the core purpose, and every phrase adds value (email-verified, explicitly authorized, fixed-taxonomy, $0, receipt caveats). No filler or repetition of schema details.

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

Completeness4/5

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

For a simple recording tool with 3 required parameters and no output schema, the description is fairly complete: it covers what is recorded, under what conditions (authorization), and the non-effects (no ranking change). It could mention the return value or how to obtain the application_id, but the schema already references apply_to_job. Overall, it's sufficient for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 67% (application_id and principal_authorized have descriptions, outcome only has enum). The description adds context like 'fixed-taxonomy' (aligning with the enum) and 'explicitly authorized' (matching principal_authorized), but does not explain the outcome values or additional parameter syntax. It adds some value but does not fully compensate for the missing outcome description.

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 a specific action ('Record'), a specific resource ('application outcome'), and scope ('one email-verified candidate's explicitly authorized, fixed-taxonomy application outcome'). It distinguishes from sibling tools like apply_to_job (which creates applications) and respond_to_interview_outcome (which handles interview responses), making it clear this is for recording a final/ongoing outcome.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (record an outcome after a candidate authorizes it, at no cost, without affecting ranking) but does not explicitly mention when to use this tool versus alternatives. It lacks explicit exclusions or guidance like 'use this instead of respond_to_interview_outcome for application-level outcomes.' The context is clear but not formally differentiated.

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

A3.9/5.0
Disambiguation4/5

Most tools have clear, distinct purposes (e.g., search_jobs vs get_job vs get_similar_jobs). The main ambiguity is between match_jobs and analyze_application_readiness, both of which assess candidate-job fit, though one ranks multiple jobs and the other evaluates readiness for a specific role. The application flow steps are well-separated.

Naming Consistency5/5

All tool names consistently follow the snake_case verb_noun pattern (e.g., get_company, list_companies, apply_to_job). Even longer names like analyze_application_readiness and compile_job_specific_resume adhere to this convention, with no mixed casing or inconsistent verb styles.

Tool Count3/5

With 20 tools, the server is on the heavier side for a job board, though the breadth of features (search, company info, salary, application, interview tracking, and product sales) partially justifies the count. Some redundancy exists (e.g., get_trending_companies vs list_companies, get_stats vs get_salary_data), making the set feel slightly bloated.

Completeness3/5

The toolset covers core job search and application workflows, but there is no way to list or track submitted applications, view application status, or withdraw an application. Post-application features are limited to interview outcomes, leaving obvious lifecycle gaps for a candidate-facing job platform.

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