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Save standard answers used on every application

save_standard_answers

Save standard answers used on every application. Right to work, sponsorship, salary expectation, notice period, relocation and more, plus answers to any specific question (answers: {question: answer}). Waiting applications that needed these get them at once. Questions about gender, ethnicity, disability and similar are never answered unless the person gives an answer. A ready-to-read summary is in say.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answersNoAnswers to specific questions: {"question text": "answer"}.
countryNoCountry the person lives in, for example United Kingdom.
pronounsNoOptional.
notice_periodNoFor example 4 weeks.
right_to_workNoFor example: Yes, British citizen.
work_locationNoWork location: remote, hybrid or on-site.
needs_sponsorshipNoSet to yes or no.
salary_expectationNoFor example £36,000.
willing_to_relocateNoSet to yes or no.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare write/openWorld/non-idempotent/non-destructive. The description adds real value beyond that: it discloses the side effect that waiting applications receive these answers immediately, a privacy rule (gender/ethnicity/disability never answered unless the person supplies one), and that a readable summary is returned in `say`. It does not address overwrite semantics, which the non-idempotent hint makes relevant.

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?

Purpose is front-loaded in the first sentence, followed by scope, side effect, privacy rule, and output location. Slightly run-on in the middle, but nearly every sentence carries distinct information and nothing is filler.

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 9-parameter write tool with a nested object and no output schema, the description covers field scope, the mutation's effect on pending applications, a privacy constraint, and where the human-readable result lives (`say`). Overwrite/repeat-save behavior is the only notable omission.

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 100%, so the schema already documents all 9 parameters. The description reinforces the field set and gives the nested-object format hint (answers: {question: answer}), but adds little meaning the schema doesn't already carry. Baseline 3 applies.

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?

States a specific verb (Save) and resource (standard answers used on every application), then enumerates the exact fields covered (right to work, sponsorship, salary, notice period, relocation). An agent can distinguish this from preference/profile siblings like set_preferences_from_text and update_profile without opening the schema.

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?

Usage is implied — save reusable answers so pending/waiting applications get them at once — but there is no explicit when-to-use, when-not, or named alternative (e.g. set_preferences_from_text or update_profile). The context signal is present but the routing guidance is left to inference.

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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