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retirement-answer-check

by vishalhabib99
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Python
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check_answer

Read-onlyIdempotent

Verify AI-drafted answers to US retirement-account questions against IRS rules and facts. Flags errors, out-of-scope topics, personal recommendations, and promissory claims to decide SEND or REVIEW.

Instructions

Check an AI-drafted answer to a US retirement-account question before it is sent to a customer.

Verifies numbers and rules (contribution limits, RMDs, rollovers, early-distribution tax and exceptions, excess contributions) against IRS-sourced facts, flags out-of-scope topics, and runs phrase rules for personal recommendations and promissory claims. Returns {"decision": "SEND" | "REVIEW", "flags": [{"type", "span", "reason", "source"}]}. Any number the facts table can't verify returns REVIEW, never SEND. The phrase rules are a baseline only: also run the advice-judge skill before treating SEND as final.

Args: question: The customer's question, verbatim. answer: The AI-drafted answer to check, verbatim. An empty answer returns REVIEW.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYes
questionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/non-destructive annotations, the description discloses important behaviors: returns a SEND/REVIEW decision with flags, any unverifiable number forces REVIEW, and an empty answer returns REVIEW. This gives the agent a realistic model of tool outcomes.

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?

The description is information-dense but tightly organized: purpose, verification scope, return shape, safety rule, caveat, then parameters. Every sentence contributes unique operational value with no filler.

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?

Despite having no output schema, the description includes the return structure inline. It covers verification domains, flag categories, failure semantics, and parameter requirements, making the tool fully actionable on its own.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must carry the load. It defines both parameters precisely: 'question: The customer's question, verbatim' and 'answer: The AI-drafted answer to check, verbatim,' plus the empty-answer behavior. This is exactly the semantic context the schema lacks.

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 opens with a specific verb and resource: 'Check an AI-drafted answer to a US retirement-account question before it is sent to a customer.' It clearly distinguishes this tool from the sibling get_facts by focusing on verifying an answer rather than retrieving facts.

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

Usage Guidelines5/5

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

The description states when to use the tool ('before it is sent to a customer') and gives an explicit caution: 'The phrase rules are a baseline only: also run the advice-judge skill before treating SEND as final.' This provides clear when-to-use and when-not-sufficient guidance.

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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