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torob-mcp

torob-mcp

by sepehr071
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Deal check

tb_deal_check
Read-onlyIdempotent

Scores whether a product price is good from 0–100 using its price history and today's competing offers, with reasons to help decide if it's a fair deal.

Instructions

Is this a good price? Score 0-100 from the product's price history and today's competing offers.

100 minus the price's percentile among past weekly minimums, minus up to 20 when above today's cheapest usable offer. reasons explains it.

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": {
      "product": "<product>"
    },
    "name": "tb_deal_check"
  }
}

Input Schema

NameRequiredDescriptionDefault
priceNoOffered price, Toman; default today's cheapest usable offer.
productYesProduct id (UUID) or torob.com product URL.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.2.0
    • changedInput schema / properties / price / description
      Previous value: -"A price in Toman you were offered; default: today's cheapest usable offer."New value: +"Offered price, Toman; default today's cheapest usable offer."
    • changedInput schema / properties / product / description
      Previous value: -"Product id (the UUID from search cards, e.g. '57ea65ae-0798-4cd0-96a7-38d8af180345'), a '/p/<id>/...' path or a full torob.com product URL."New value: +"Product id (UUID) or torob.com product URL."
  2. First observedv0.1.1

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so the safety profile is covered. The description goes further by disclosing the actual scoring model (100 minus price percentile among past weekly minimums, minus up to 20 when above today's cheapest usable offer) and noting a `reasons` field, which is real behavioral insight not captured in annotations.

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?

Roughly 40 words, front-loaded with the question and the output form, with zero filler. The formula sentence is somewhat dense in notation ('minus the price's percentile...') and slightly harder to parse than a plain-language restatement would be, but nothing is redundant.

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 2-parameter, annotation-covered, no-output-schema tool, the definition covers what the tool returns and how it is computed. The only minor gap is not stating what `reasons` contains or what the score means in practical terms (e.g., thresholds for 'good'), though the return-format burden is light here.

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%, including the 'default today's cheapest usable offer' behavior and the Toman unit, so the schema carries most parameter meaning. The description reinforces the comparison basis (past weekly minimums vs today's competing offers) but adds no syntax or format detail beyond the schema; 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?

Opens with the user-facing question the tool answers and states a concrete output: a 0-100 score derived from the product's price history plus today's competing offers. That framing (a judgment/score) is clearly distinct from sibling tools like tb_price_history, tb_cheapest, tb_compare, and tb_best_value, so an agent can route 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 rather than stated: it is for judging whether a specific offered price is fair. There is no explicit when-to-use/when-not-to-use guidance and no sibling is named as an alternative (e.g., 'for raw series, use tb_price_history'), which leaves the agent to infer the boundary.

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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