digikala-mcp

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Best picks for a budget

dk_best_for_budget

Read-onlyIdempotent

Rank in-stock Digikala products within a Toman budget by weighted rating, with visible reasoning for budget product searches.

Instructions

Rank the best-rated in-stock products within a budget, with the reasoning shown.

Ranks by a weighted rating: a product's rating pulled toward the average of the candidates until it has about 20 ratings, so a 5.0 from 3 buyers does not beat a 4.6 from 900. Ties go to the cheaper one. For a query only titles with every query word count, and accessories are dropped. Use for "best X under Y Toman"; then dk_product on a pick for every seller's price, dk_reviews for what buyers say.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max picks to return.
`pages`	No	Result pages to scan, 20 products each.
`query`	No	What to buy, e.g. 'هدفون بی سیم' or 'گوشی سامسونگ'.
`max_price`	Yes	Budget in Toman, e.g. 20000000 for 20 million Toman.
`min_price`	No	Skip anything cheaper, in Toman. Default for a query: a floor that drops accessories.
`min_ratings`	No	Skip products with fewer ratings than this.
`category_code`	No	Or a category code instead of a query, e.g. 'mobile-phone' (from dk_suggest).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.2.1Oct 5, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations: it discloses the shrinkage ranking algorithm (rating pulled toward candidate average until ~20 ratings), the cheaper-wins tie-break, the all-query-words-in-title match rule, and that accessories are dropped. This is exactly the non-obvious behavior an agent needs to trust and interpret the output.

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?

Three dense sentences, front-loaded with purpose, then algorithm, then usage/follow-ups. No filler, though the middle sentence packs several rules together and reads slightly heavy.

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?

An output schema exists so return format need not be described; annotations cover safety. Purpose, ranking behavior, query semantics, and downstream tool routing are all present, leaving nothing an agent needs to invoke this correctly.

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%, so the baseline is 3, but the description adds real meaning: it explains that min_price defaults to a floor that drops accessories when a query is present, and clarifies the query-title matching semantics that govern how 'query' behaves.

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?

Specific verb ('rank') plus resource ('best-rated in-stock products') plus scope ('within a budget'), and the ranking criterion is stated. An agent can distinguish this from dk_best_sellers and dk_find_cheapest without opening any schema.

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

Usage Guidelines4/5

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

Gives an explicit trigger phrase ('best X under Y Toman') and routes follow-ups (dk_product for seller prices, dk_reviews for buyer sentiment). It lacks an explicit when-not / alternative-tool condition (e.g., vs. dk_best_sellers or dk_find_cheapest), so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.