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Roth Conversion Calculator

Check if a request fits

check_request
Read-onlyIdempotent

Say in plain words what the user wants (e.g. 'I'm still working and want to know Roth vs traditional 401k'). Returns whether this optimizer can do it and, if not, the closest thing it can do (or the sister server that can). Fast, no solve.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "description": "<description>"
    },
    "name": "check_request"
  }
}

Input Schema

NameRequiredDescriptionDefault
descriptionYesThe user's question in a sentence or two.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: the call performs no solving and is fast, and the response carries either a yes plus closest match or a pointer to a sister server — useful for an agent deciding whether to spend a solve.

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?

Two tight sentences, front-loaded with the input instruction then the return shape; there is no filler. Minor deduction for the double parenthetical in the second sentence, which is slightly awkward to parse.

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?

With no output schema, the description must convey the return contract, and it does at a usable level: a capability verdict plus either the nearest supported task or a redirect to a sister server. It does not describe the response's structure or exact field names, which is a small remaining gap for a routing tool.

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 100% and the single parameter is self-documenting, so the baseline is 3. The description adds a concrete phrasing instruction with a worked example ('I'm still working and want to know Roth vs traditional 401k'), which clarifies the expected abstraction level of the input beyond the schema's generic 'question in a sentence or two'.

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 and outcome: it returns whether this optimizer can handle the described request, and if not, the closest capability or sister server. That clearly separates it from optimize_roth_conversions (which actually solves) and get_conversion_example (which presumably shows an example), though no sibling is named explicitly.

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?

Implies the when: describe the user's intent in plain words as a cheap pre-check ('Fast, no solve'), which suggests calling before a real solve. It never states when not to use it, nor names a sibling tool as the follow-up alternative, so routing guidance is inferred rather than explicit.

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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