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cenogram-mcp-server

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by cenogram

get_price_statistics

Read-only

Retrieve price per m² statistics for residential apartments in Poland using transaction prices from notarial deeds. Filter by location to analyze market-based median and average prices.

Instructions

Get price per m² statistics by location for residential apartments in Poland. Note: only covers residential units (lokale mieszkalne). For other property types, use search_transactions. 'Warszawa'/'Kraków'/'Łódź' auto-expand to all sub-districts (Warszawa=19, Kraków=5, Łódź=6). Other names use partial match. Data quality: based on transaction prices from notarial deeds, not asking/listing prices. Coverage varies by county (some have data gaps of 5+ years). Note: median/average prices are market-based — fractional ownership shares and non-market deeds (public tenders, foreclosures, privileged/subsidized sales) are excluded from price aggregates. Transaction counts and coverage stay complete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationNoFilter by location name. 'Warszawa'/'Kraków'/'Łódź' auto-expand to all sub-districts. Other names use case-insensitive partial match (e.g. 'Wrocł' matches 'Wrocław'). Omit for all Poland.
Behavior5/5

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

Annotations already declare readOnlyHint and destructiveHint, but description adds critical context: data source (notarial deeds), coverage gaps, exclusion of fractional ownership and non-market deeds from price aggregates, and retention of transaction counts.

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?

Well-structured with clear notes, though slightly verbose. Every sentence earns its place, providing essential context without redundancy.

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?

Despite no output schema, description thoroughly covers what statistics are provided, data quality, and exclusions, making it complete for usage.

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% for the single parameter, but description adds valuable behavioral details about auto-expansion and partial matching, going beyond the schema description.

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?

Clearly states the tool retrieves price per m² statistics for residential apartments in Poland, distinguishing it from sibling tools like search_transactions which handle other property types.

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

Usage Guidelines5/5

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

Explicitly specifies it only covers residential units and directs to search_transactions for other property types. Also details auto-expansion for major cities and partial match behavior.

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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