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sepehr071

masterkala-mcp

by sepehr071
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Specs and comparison

mk_specs
Read-only

Compare up to four products side by side to get their specification table and stock counts. Look up battery life, Bluetooth version, or any spec row by product ID, including missing IDs.

Instructions

Get the specification table of 1-4 products side by side, with each product's stock count.

Use for "battery life / Bluetooth version of X" or to compare products row by row. Each spec row has one value per product id; a missing id means no value for that product. Ids that do not exist are listed in not_found.

Input Schema

NameRequiredDescriptionDefault
product_idsYes1 to 4 product ids, e.g. [27144, 20850].

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.3.0
    • removedInput schema / properties / product_ids / title
      Removed value: -"Product Ids"
    • removedInput schema / title
      Removed value: -"mk_specsArguments"
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "title": "mk_specsDictOutput",
-  "type": "object"
-}New value: +null
  2. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered; the description adds genuinely new behavioral detail: the 1-4 product cap, that a missing product id in a row means no value, and that nonexistent ids are surfaced in not_found. These edge-case and error-handling semantics are exactly the kind of context structured fields don't carry.

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 core capability is front-loaded in sentence one, followed by usage triggers and then response-shape caveats. No filler, no repetition of the schema, and every sentence adds information.

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 carries the burden of explaining the return shape, and it does: a spec table with per-product values and a not_found list. Minor gaps remain around value formatting/units and whether stock count is always present, but nothing needed to call it correctly is missing.

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 fully documented, so baseline would be 3. The description earns the extra point by extending product_ids semantics into the response: one value per id, absent ids produce no value, invalid ids land in not_found.

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 (get) and resource (specification table) with explicit scope: 1-4 products side by side plus stock count. This cleanly distinguishes it from generic siblings like mk_product or mk_search, which would not return a comparative row-per-spec table.

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 concrete triggering use cases ('battery life / Bluetooth version of X', row-by-row comparison), which is clear contextual guidance. It stops short of naming an alternative sibling (e.g., when to prefer mk_product for a single product), so it is not a full when/when-not treatment.

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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