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MCP Market Russia

calculate_cost

Calculate estimated construction cost based on real market data from the catalog. Uses average price per m² by material and region from actual company prices and projects.

Args: area: House area in square meters (required, e.g. 120) material: Building material (каркас/frame, брус/timber, газобетон/aerated_concrete, кирпич/brick, СИП/SIP). Empty = average across all. region: Region or city name for regional pricing. Empty = nationwide average. floors: Number of floors (1 or 2). 0 = no adjustment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaYes
floorsNo
regionNo
materialNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the data source ('real market data from the catalog') and behavior ('uses average price per m² by material and region'), but does not explicitly state whether the operation is read-only, has side effects, or other behavioral traits. For a calculator, this is acceptable but not rich.

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?

The description is well-organized with an opening sentence and a clear Args list. It is slightly longer than necessary but every sentence earns its place by documenting parameters. Front-loaded with the core purpose.

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?

Given the output schema exists, return value details are covered. The description provides thorough input parameter semantics and context. It lacks usage guidelines and explicit behavioral caveats, but for a simple calculation tool with clear inputs and an output schema, it is largely complete.

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 fully compensates. It explains every parameter: area (required, units), material (list of allowed values and default), region (string and default behavior), and floors (allowed values and meaning of 0). This adds significant meaning beyond the raw schema.

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 clearly states a specific verb+resource: 'Calculate estimated construction cost based on real market data from the catalog.' It also distinguishes itself by mentioning the methodology (average price per m² by material and region), which separates it from sibling tools like project_estimator or price_comparison.

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?

The description implies usage through detailed parameter explanations but does not explicitly state when to use this tool versus alternatives. No alternatives or exclusions are mentioned, so it only provides implied usage context.

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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