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Generate Screening Answers

generate_qa_answers

Generate batch answers to screening and outreach questions for job applications, using your resume and referral contact for context.

Instructions

Generate a batch of screening-question answers for an application. questions is the complete list of questions to answer in this batch. Outreach/cold messages are asked as free-form questions here (e.g. "Write a short LinkedIn DM to the hiring manager") — answers are grounded in the resume and the application's linked referral contact when one exists.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionsYes
application_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.11.1

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and openWorldHint=true, so the write/creative nature is covered structurally. The description adds genuinely useful context that answers are grounded in the resume and the application's linked referral contact, but it omits return format, cost/latency, and any persistence behavior.

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?

Front-loaded with the core purpose, then two supporting sentences with no filler. Slightly verbose in the final clause but every sentence contributes.

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

Completeness3/5

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

There is no output schema, so the description ideally would explain what the generated answers look like or where they go, which it does not. It is adequate for a generation tool but leaves the return/consumption story and application_id semantics unaddressed.

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 coverage is 0%, so the description must carry the burden. It meaningfully explains `questions` (the complete list for this batch, including free-form outreach prompts), but says nothing about `application_id`, leaving half the required parameters undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Generate a batch of screening-question answers for an application'), which clearly identifies the operation. It does not explicitly differentiate from siblings like list_qa_entries or record_filled_answers, but the purpose is unambiguous.

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

Usage Guidelines2/5

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

No when-to-use guidance, no prerequisites, and no alternatives named. It clarifies the content of `questions` and notes that outreach messages surface here as free-form questions, but this is context about inputs, not routing guidance for selecting this tool over its 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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